量子计算与过程神经网络研究及应用
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摘要
量子计算是信息科学和量子理论相结合的新兴交叉学科。依托量子计算基本原理产生的量子算法,以其独特的优化性能受到世界各国学者的普遍关注,并以显示出十分广阔的应用前景。过程神经元网络是根据生物神经系统信息处理机制并结合实际问题的应用背景提出的一种新的人工神经网络模型。网络的过程式输入放宽了传统神经元网络模型对输入的同步瞬时限制,是传统神经元网络在时间域上的扩展,是更一般化的人工神经元网络模型。本论文重点研究了量子计算与神经网络和智能优化算法的融合机制，以及过程神经网络训练算法,主要研究内容可归纳如下：
第一,通过将量子计算和神经网络理论相融合,提出一种基于量子旋转门和受控非门的量子BP网络模型及算法,证明了该模型的连续性,通过太阳黑子数预测的实验结果表明,该模型的非线性预测能力明显优于普通BP网络；通过模拟生物神经元的信息处理机制,提出一种具有量子权值和量子活性值的神经网络模型及超线性收敛学习算法,通过模式识别和函数逼近验证了模型的有效性；通过分析量子门物理意义,提出一种量子门节点神经网络模型及算法,该模型具有隐含的多重吸引子,可明显增加收敛概率,仿真结果表明其收敛速度和预测能力明显优于普通神经网络。
第二,针对过程神经网络模型的训练问题,提出一种基于勒让德正交基函数的过程神经网络学习算法,该方法可有效解决复杂的时空聚合运算问题；针对海量样本的并行处理问题,提出了并联过程神经网络预测模型及算法,该方法既能分散网络负载,又能提高单个网络的预测能力。
第三,在分析目前量子搜索算法存在问题的基础上,通过构造新的量子搜索引擎,提出了两种改进算法。首先,提出了一种目标加权搜索算法,该算法可使得到每个目标的概率与目标的重要程度相一致,使越是重要的目标,成功概率越大；其次,提出了一种基于小相位旋转的量子搜索算法,通过将旋转相位固定为0.01π,可使算法的成功概率最低为99.99%。
第四,通过分析目前量子进化算法存在的问题,提出一种基于量子位球面坐标的量子遗传算法,设计了两种新的量子门算子。在该算法中,通过将量子位不同维的坐标均视为优化问题的近似解,该算法可以增加对解空间的遍历性,提高收敛的概率。通过直接将量子位的相位视为待优化个体的编码,分别提出了基于相位编码的量子遗传算法、量子免疫算法、量子蚁群算法和量子粒子群算法。在这些算法中,由于优化过程统一在[-1,1]n或[0,2π]n进行,与具体问题无关,因此对不同尺度空间的优化问题具有良好的适应性。通过将这些算法融合到过程神经网络的训练中,可明显提高模型的计算效率和预测精度。
第五,对比研究了基于相位编码的量子遗传算法、量子蚁群算法、量子粒子群算法、带精英保留策略的遗传算法、量子BP神经网络、量子权值神经网络、量子门节点神经网络、普通BP网络等8种预测模型在径流中长期预报中的建模应用。通过对漫湾水电站52年月径流系列及洪家渡水电站55年月径流系列的预测结果表明,量子智能优化算法的预报性能明显优于普通遗传算法,量子神经网络的预报性能明显优于普通BP网络。从而验证了量子机制的引入可以改善传统模型的预报性能,并提高月径流时间序列的预报精度。
最后,对全文的研究工作进行了总结,并对有待于进一步研究的问题进行了展望。
Abstract
Quantum computation is a novel inter-discipline that includes quantum mechanics and information science.The quantum algorithm based on the basic principles of quantum computation has been widely concerned by scholars around the; world,and has shown the very broad application prospects.Process neural network (PNN) is a new artificial neural network (ANN) model proposed at the beginning of this century according to information processing mechanism of the biological nervous system in conjunction with the application of practical problems.The process, input of the PNN remove the instantaneous synchronization constraints for input in traditional ANN model. The process neural network is an extension model of traditional ANN in the time domain, and it is a more generalized ANN model. The research content of this thesis can be summarized as follows.
Firstly, a quantum BP neural networks model and algorithm based on quantum rotation gates and quantum controlled-not gates are proposed, and then the continuity of the model is proved The experimental results of the sunspot number prediction show that the predictive power of this model is superior to common BP networks. By simulating biological neural information processing mechanism, a quantum weight neural network model is presented with both the quantum linked weight and the quantum activation value.Using gradient descent algorithm, a super-linearly convergent learning algorithm of this model is designed. The availability of the approach is illustrated by two application examples of pattern recognition and function approximation; by analyzing the physical meaning of quantum gates, a quantum gates neural network model and algorithm are introduced with quantum gate nodes.This model can significantly increase the probability of convergence because of its implicit multi-attractors.Simulation results show that the convergence speed and prediction capabilities are significantly better than that of the ordinary ANN.
Secondly, the learning algorithms of PNN are developed based on Legendre orthogonal basis functions, which can effectively solve complex computing problems of spatial and temporal aggregation. For the issue of parallel processing of massive samples, a parallel process neural networks model and algorithm are constructed. This method can not only decentralized networks load, but also improve the predictive power of a single networks.
Thirdly, on the basis of analyzing the existing problems in current quantum search algorithm, two improved algorithms are proposed by constructing the new quantum search engines.A quantum search algorithm is presented based on weighted targets, in which the successful probability of each marked item is consistent with the corresponding weight coefficient. Namely, the greater successful probability is gotten for the more important target. An improved quantum search algorithm with small phase rotations is proposed. When the size of phase rotations are fixed at 0.01π, the success probability, at least 99.99% can be obtained.
Fourthly, by analying the problems existing in current quantum evolutionary algorithm, a quantum genetic algorithm is proposed based on the spherical coordinates of quantum bit, and two new quantum gate operators are designed. In this algorithm, by regarding coordinates of the qubit as approximate solutions of the optimization problem, this algorithm can increase the solution space traversal arid the probability of convergence.By directly taking the qubit phase as a gene on chromosome,four quantum-inspired optimization algorithms are respectively presented. In these algorithms, the optimization process is performed in [-1,1]n or [0,2π]n,which has nothing to do with specific issues, therefore, the proposed methods have good adaptability for a variety of optimization problems.By combining these algorithms into the process of neural network training, the computational efficiency and prediction accuracy of model can be significantly improved.
Fifthly, on the basis of the Mid-and-Long term forecasting of monthly discharge time series, the simulation comparisons of eight intelligent optimization models are studied. Through applying these model to the two monthly discharge time series of hydropower station of the Manwan and the Hongjiadu, the prediction results show that the quantum-inspired optimization algorithms are superior to ordinary genetic algorithm,and quantum neural networks are superior to common BP neural networks, which verifies the introduction of quantum mechanisms can improve the forecasting performance of the traditional model and algorithm,and can increase the forecasting accuracy of monthly discharge time series. Finally, all the studies in this text were summarized, and some new topics are discussed.
第一,通过将量子计算和神经网络理论相融合,提出一种基于量子旋转门和受控非门的量子BP网络模型及算法,证明了该模型的连续性,通过太阳黑子数预测的实验结果表明,该模型的非线性预测能力明显优于普通BP网络；通过模拟生物神经元的信息处理机制,提出一种具有量子权值和量子活性值的神经网络模型及超线性收敛学习算法,通过模式识别和函数逼近验证了模型的有效性；通过分析量子门物理意义,提出一种量子门节点神经网络模型及算法,该模型具有隐含的多重吸引子,可明显增加收敛概率,仿真结果表明其收敛速度和预测能力明显优于普通神经网络。
第二,针对过程神经网络模型的训练问题,提出一种基于勒让德正交基函数的过程神经网络学习算法,该方法可有效解决复杂的时空聚合运算问题；针对海量样本的并行处理问题,提出了并联过程神经网络预测模型及算法,该方法既能分散网络负载,又能提高单个网络的预测能力。
第三,在分析目前量子搜索算法存在问题的基础上,通过构造新的量子搜索引擎,提出了两种改进算法。首先,提出了一种目标加权搜索算法,该算法可使得到每个目标的概率与目标的重要程度相一致,使越是重要的目标,成功概率越大；其次,提出了一种基于小相位旋转的量子搜索算法,通过将旋转相位固定为0.01π,可使算法的成功概率最低为99.99%。
第四,通过分析目前量子进化算法存在的问题,提出一种基于量子位球面坐标的量子遗传算法,设计了两种新的量子门算子。在该算法中,通过将量子位不同维的坐标均视为优化问题的近似解,该算法可以增加对解空间的遍历性,提高收敛的概率。通过直接将量子位的相位视为待优化个体的编码,分别提出了基于相位编码的量子遗传算法、量子免疫算法、量子蚁群算法和量子粒子群算法。在这些算法中,由于优化过程统一在[-1,1]n或[0,2π]n进行,与具体问题无关,因此对不同尺度空间的优化问题具有良好的适应性。通过将这些算法融合到过程神经网络的训练中,可明显提高模型的计算效率和预测精度。
第五,对比研究了基于相位编码的量子遗传算法、量子蚁群算法、量子粒子群算法、带精英保留策略的遗传算法、量子BP神经网络、量子权值神经网络、量子门节点神经网络、普通BP网络等8种预测模型在径流中长期预报中的建模应用。通过对漫湾水电站52年月径流系列及洪家渡水电站55年月径流系列的预测结果表明,量子智能优化算法的预报性能明显优于普通遗传算法,量子神经网络的预报性能明显优于普通BP网络。从而验证了量子机制的引入可以改善传统模型的预报性能,并提高月径流时间序列的预报精度。
最后,对全文的研究工作进行了总结,并对有待于进一步研究的问题进行了展望。
Abstract
Quantum computation is a novel inter-discipline that includes quantum mechanics and information science.The quantum algorithm based on the basic principles of quantum computation has been widely concerned by scholars around the; world,and has shown the very broad application prospects.Process neural network (PNN) is a new artificial neural network (ANN) model proposed at the beginning of this century according to information processing mechanism of the biological nervous system in conjunction with the application of practical problems.The process, input of the PNN remove the instantaneous synchronization constraints for input in traditional ANN model. The process neural network is an extension model of traditional ANN in the time domain, and it is a more generalized ANN model. The research content of this thesis can be summarized as follows.
Firstly, a quantum BP neural networks model and algorithm based on quantum rotation gates and quantum controlled-not gates are proposed, and then the continuity of the model is proved The experimental results of the sunspot number prediction show that the predictive power of this model is superior to common BP networks. By simulating biological neural information processing mechanism, a quantum weight neural network model is presented with both the quantum linked weight and the quantum activation value.Using gradient descent algorithm, a super-linearly convergent learning algorithm of this model is designed. The availability of the approach is illustrated by two application examples of pattern recognition and function approximation; by analyzing the physical meaning of quantum gates, a quantum gates neural network model and algorithm are introduced with quantum gate nodes.This model can significantly increase the probability of convergence because of its implicit multi-attractors.Simulation results show that the convergence speed and prediction capabilities are significantly better than that of the ordinary ANN.
Secondly, the learning algorithms of PNN are developed based on Legendre orthogonal basis functions, which can effectively solve complex computing problems of spatial and temporal aggregation. For the issue of parallel processing of massive samples, a parallel process neural networks model and algorithm are constructed. This method can not only decentralized networks load, but also improve the predictive power of a single networks.
Thirdly, on the basis of analyzing the existing problems in current quantum search algorithm, two improved algorithms are proposed by constructing the new quantum search engines.A quantum search algorithm is presented based on weighted targets, in which the successful probability of each marked item is consistent with the corresponding weight coefficient. Namely, the greater successful probability is gotten for the more important target. An improved quantum search algorithm with small phase rotations is proposed. When the size of phase rotations are fixed at 0.01π, the success probability, at least 99.99% can be obtained.
Fourthly, by analying the problems existing in current quantum evolutionary algorithm, a quantum genetic algorithm is proposed based on the spherical coordinates of quantum bit, and two new quantum gate operators are designed. In this algorithm, by regarding coordinates of the qubit as approximate solutions of the optimization problem, this algorithm can increase the solution space traversal arid the probability of convergence.By directly taking the qubit phase as a gene on chromosome,four quantum-inspired optimization algorithms are respectively presented. In these algorithms, the optimization process is performed in [-1,1]n or [0,2π]n,which has nothing to do with specific issues, therefore, the proposed methods have good adaptability for a variety of optimization problems.By combining these algorithms into the process of neural network training, the computational efficiency and prediction accuracy of model can be significantly improved.
Fifthly, on the basis of the Mid-and-Long term forecasting of monthly discharge time series, the simulation comparisons of eight intelligent optimization models are studied. Through applying these model to the two monthly discharge time series of hydropower station of the Manwan and the Hongjiadu, the prediction results show that the quantum-inspired optimization algorithms are superior to ordinary genetic algorithm,and quantum neural networks are superior to common BP neural networks, which verifies the introduction of quantum mechanisms can improve the forecasting performance of the traditional model and algorithm,and can increase the forecasting accuracy of monthly discharge time series. Finally, all the studies in this text were summarized, and some new topics are discussed.
引文
[1]Shor P W.Algorithms for quantum computation:Discrete logarithms and factoring[C].Proc. of the 35th Annual Symp.on Foundations of Computer Science,New York,USA,1994: 124-134.
[2]Grover L K.A fast quantum mechanical algorithm for database search[C].Proc.of the 28th Annual ACM Symp.on Theory of Computing,New York,USA,1996;212-219.
[3]Wang Z G,Wong Y S,Rahman M.Development of a parallel optimization method based on genetic simulated annealing algorithm[J].Parallel Computing,2005,31(8-9):839-857.
[4]Suman B.Study of simulated annealing based algorithms for multi objective optimization of a constrained problem[J].Computers & Chemical Engineering,2004,28(9):1849-1871.
[S]Soliman S A,Mantaway A H,El-Hawary M E.Simulated annealing optimization algorithm for power systems quality analysis[J].International Journal of Electrical Power & Energy Sysems,2004,26(1):31-36.
[6]Torrecilla J S,Otero L,Sanz P D.Optimization of an artificial neural network for thermal/pressure food processing:Evaluation of training algorithms[J].Computers and Electronics in Agriculture,2007,56(2):101-110.
[7]Sexton R S,McMurtrey S,Cleavenger D.Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem[J].European Journal of Operational Research,2006; 168(3):1009-1018.
[8]Sexton R S,Dorsey R E,Sikander N A.Simultaneous optimization of neural network function and architecture algorithm[J].Decision Support Systems,2004,36(3):283-296.
[9]Zhang Z H.Immune optimization algorithm for constrained nonlinear multiobjective optimization problems[J].Applied Soft Computing,2007,7(3):840-857.
[10]Zhang Z H.Constrained multiobjective optimization immune algorithm:Convergence and application[J].Computers & Mathematics with Applications,2006,52(5):791-808.
[11]Guo Z L,Wang S A,Zhuang J.A novel immune evolutionary algorithm incorporating chaos optimization[J].Pattern Recognition Letters,2006,27(1):2-8.
[12]Marseguerra M,Zio E,Cadini F.Genetic algorithm optimization of a model-free fuzzy control system[J].Annals of Nuclear Energy,2005,32(7):712-728.
[13]Fantinutto R,Guglieri G,Quagliotti F B.Flight control system design and optimization with a genetic algorithm[J].Aerospace Science and Technology,2005,9(1):73-80.
[14]Lewin D R,Parag A.A constrained genetic algorithm for decentralized control system structure selection and optimization[J].Automatica,2003,39(10):1801-1807.
[15]Martinez C G,Cordon O,Herrera F.A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP[J].European Journal of Operational Research,2007,180(1):116-148.
[16]Demirel N C,Toksari M D.Optimization of the quadratic assignment problem using an ant colony algorithm[J].Applied Mathematics and Computation,2006,183(1):427-435.
[17]Dreo J,Siarry P.An ant colony algorithm aimed at dynamic continuous optimization[J]. Applied Mathematics and Computation,2006,181(1):457-467.
[18]Duan H B,Wang D B,Yu X F.Novel approach to nonlinear PID parameter optimization using ant colon optimization algorithm[J].Journal of Bionic Engineering,2006,3(2):73-78.
[19]Zhang J R,Zhang J,Lok T M.A hybrid particle swarm optimization back-propagation algorithm for feedforward neural network training[J].Applied Mathematics and Computation, 2007,185(2):1026-1037.
[20]Lian Z G,Jiao B,Gu X S.A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan[J].Applied Mathematics and Computation,2006，183(2): 1008-1017.
[21]Zhao B,Guo C X,Bai B R.An improved particle swarm optimization algorithm for unit commitment[J].International Journal of Electrical Power & Energy Systems,2006,28(7): 482-490.
[22]Yin P Y,Yu S S,Wang P.A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems[J].Computer Standards & Interfaces,2006,28(4):441-450.
[23]Narayanan A,Moore M.Quantum-inspired Genetic Algorithms[C].Proceedings of IEEE International Conference on Evolutionan Computation,Nagoya,Japan,1996:61-66.
[24]Han K H,Kim J H.Genetic quantum algorithm and its application to combinational optimization problem[C].Proceedings of the International Congress on Evolutionary Computation,2000:1354-1360.
[25]Han K H,Kim J H.Analysis of quantum-inspired evolutionary algorithm[C].Proceedings of the International Congress on Artificial Intelligence,2001:727-730.
[26]Sepctor L,Barnum H,Bernstein H J.Finding a better than classical quantum AND/OR algorithm using genetic programming[C].Proceedings of the 1999 International Congress on Evolutionary Computation,1999:2239-2246.
[27]李承祖.量子信息和量子计算[M].国防科技大学出版社，2000.
[28]夏培肃.量子计算[J].计算机研究与发展，2001，38(10)：1153-1171.
[29]李士勇，李盼池.量子计算与量子智能优化算法[M].哈尔滨工业大学出版社，2009.
[30]Bshouty N,Jackson J.Learning DNF over the uniform distribution using a quantum ekample orcle[C].Proc.of the 8th Ann.Conf.Computional Learning Theory,New York,1995.
[31]Farhi E,Gutmann S.Quantum computation and decision trees[J].Physical Review A,1998, 58(2):915-928.
[32]He X G,Liang J Z.Procedure Neural Networks[C].The 16th World Computer Congress 2000, Proceedings of the Conference on Intelligent Information Processing,Beijing,2000:143-146.
[33]何新贵,梁久祯.过程神经元网络的若干理论问题[J].中国工程科学，2000，2(12)：40-44.
[34]Benioff P.Quantum mechanical hamiltonan models of Turing machines[J].Journal of Statistical Physics,1982,29(3):515-546.
[35]Kak S C.On quantum neural computing[J].Information Science,1995,83,(3-4):143-160.
[36]Toth G.Quantum cellular neural networks[J].Supertattices and Microstructures,1996,20(4): 473-478.
[37]Gopathy P,Nicolaos B.Quantum neural networks (QNNs) inherently fuzz feedforward neural networks[J].IEEE Transactions on Neural Networks,1997,8(3):6179-693.
[38]Lagaris I E.Likas A,Fotiadis D I.Artificial neural network methods in quantum mechanics[J]. Computer Physics Communications,1997,104(1-3):1-14.
[39]Ventura D,Tony M.Quantum associative memory with exponential capacity[C].Proceedings of the International Joint Conference on Neural Networks,Anchorage,Alaska,1998:509-513.
[40]Veritura D,Tony M.Quantum associative memory[J].Information Science,2000,124(1-4): 273-296.
[41]Li W G.A study of parallel neural networks[C].Proceedings of the Internatignal Joint Conference on Neural Networks,1999:1113-1116.
[42]Behrman E C,Steck J E,Skinner S R.A spatial quantum neural computer[C].Proceedings of the International Joint Conference on Neural Networks,1999:874-877.
[43]Ajit N,Tammy M.Quantum artificial neural network architectures and components[J]. Information Science,2000,128(3-4):231-255.
[44]Matsui N,Kouda N,Nishimura H.Neural network based on QBP and its performance[C]. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000:247-252.
[45]Altaisky M V.Quantum neural network[J].quart-ph/0107012,2001,1-4.
[46]Kouda N,Matsui N,Nislimura H.Qubit neural network and its learning efficiency[J].Neural Computing & Applications,2005,14(2):114-121.
[47]Maeda M,Suenaga M,Miyajima H.Qubit neuron according to quantum circuit for XOR problem[J].Applied Mathematics and Computation,2007,185(2):1015-1025.
[48]Shafee F.Neural networks with quantum gated nodes[J].Engineering Applications of Artificial Intelligence,2007,20(4):429-437.
[49]解光军,庄镇泉.量子神经网络[J].计算机科学,2001,28(7)：1-6.
[50]解光军.量子神经计算[J].合肥工业大学学报(自然科学版)，2002，25(3)：355-359.
[51]解光军，庄镇泉.量子神经计算模型研究[J].电路与系统学报，2002，7(2)：83-88.
[52]解光军，杨俊安,庄镇泉.基于量子双缝干涉实验的神经网络模型[J].模式识别与人工智能，2003，16(1)：28-32.
[53]解光军.李斌，庄镇泉.量子系统实现神经计算的理论分析[J].电子与信息学报,2003,25(5)：606-611.
[54]解光军，周典，范海秋.基于量子门组单元的神经网络及其应用[J].系统工程理论与实践,2005，(5)：113-117.
[55]解光军.量子神经计算技术[J].吉首大学学报(自然科学版),2005,26(1):3-7.
[56]钟艳花，余永权，余晓敏.量子神经动力学分析[J].量子电子学报，2005，22(2)：192-195.
[57]李飞,赵生妹，郑宝玉.量子神经网络及其在CDMA多用户检测中的应用[J].信号处理,2005,21(6)：555-559.
[58]朱大奇，桑庆兵.光电雷达电子部件的量子神经网络故障诊断算法[J].电子学报，2006,34(3)：573-576.
[59]李盼池,李士勇.一种量子自仴织特征映射网络模型及聚类算法[J].量子电子学报,2007,24(4)：463-468.
[60]吕强，俞金寿.量子神经网络软测量模型及应用[J].系统仿真学报，2007，19(24)：5696-5699.
[61]Li P C,Li S Y.Learning algorithm and application of quantum BP neural networks based on universal quantum gates[J].Journal of Systems Engineering and Electronics,2008,19(1): 167-174.
[62]周日贵,姜楠，丁秋林.量子Hopfield神经网络及图像识别[J].中国图象图形学报,2068,13(1)：119-123.
[63]李盼池.一种量子神经网络模型学习算法及应用[J].控制制理论与应用，2009，26(5)：531-534.
[64]李昐池.量子计算及其在智能优化与控制中的应用[D].哈尔滨：哈尔滨工业大学，2009.
[65]李云红,谭阳红.基于小波包与量子神经网络的容差模拟电路的软故障诊断[J].微电子学与计算机,2009,26(5):187-192.
[66]Han K H,Park K H,Lee C H.Parallel quantum-inspired genetic algorithm for combinatorial optimization problem[C].Proceedings of the International Congress on Evolutionary Computation,2001:1422-1429.
[67]Han K H,Kim J H.Quantum-inspired evolutionary algorithm for a class of combinatorial optimization [J].IEEE Transactions on Evolutionary Computation,2002,6(6):580-593.
[68]Talbi H,Draa A,Batouche M.A new quantum-inspired genetic algorithm for solving the travelling salesman problem[C].Proceedings of the International Conference on Industrial Technology,2004:1192-197.
[69]Khorsand A R,Akbarzadeh M R.Quantum gate optimization in a meta-level genetic quantum algorithm[C].2005 IEEE International Conference on Systems,Man and Cybernetics,2005: 3055-3062.
[70]Moore P,Venavagamoorthy G K.Evolving combinational logic circuits using a hybrid quantum evolution and particle swarm inspired algorithm[C].Proceedings of the NASA/DoD Conference on Evolvable Hardware,2005:97-102.
[71]Mikki S M,Kishk A A.Quantum particle swarm optimization for electromagretics[J].IEEE Transactions on Antennas and Propagation,2006,54(10):2764-2775.
[72]Zhang G X,Jin W D,Hu L Z.A novel parallel quantum genetic algorithm[C].Proceedings of the 4th International Conference on Parallel and Distributed Computing,Applications and Technologies,2003:693-697.
[73]Yang J.A,Li B,Zhuang Q.Multi-universe parallel quantum genetic algorithm and its application to blind-source separation[C].Proceedugs of the International Conference on Neural Networks and Signal Processing,2003:393-398.
[74]Chen H,Zhang J H,Zhang C.Chaos updating rotated gates quantum-inspired genetic algorithm[C].Proceedings of the International Conference on Communications,Cuircuits and Systems,2004:1108-1112.
[75]Yang S Y,Wang M,Jiao L C.A genetic algorithm based on quantum chromosome[C]. Proceedings of the 7th International Conference on Signal Processing,2004:1622-1625.
[76]Wang L,Tang F,Wu H.Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation[J].Applied Mathematics and Computation,2005, 171(2):1141-1156.
[77]王凌,刘波.微粒群优化与调度算法[M].清华大学出版社,2008.
[78]李士勇,李盼池.基于实数编码和目标函数梯度的量子遗传算法[J].哈尔滨工业大学学报，2006，38(8)：1216-1218，1223.
[79]Li P C,Li S Y.Quantum-inspired evolutionary algorithm for continuous spaces optimization[J].Chinese Journal of Electronics,2008;17(1):80-84.
[80]李盼池,李士勇.求解连续空间优化问题的量子蚁群算法[J].控制理论与应用,2008,25(2)：237-241.
[81]李盼池，李士勇.求解连续空间优化问题的混量子免疫算法[J].模式识别与人工智能,2007,20(5)：654-660.
[82]李士勇，李盼池.求解连续空间优化向题的量子粒子群算法[J].量子电子学报,2007,24(5)：569-574.
[83]Li P C,Li S Y.Quantum-inspired evolutionary algorithm for continuous spaces optimization based on Bloch coordinates of gubits[J].Neurocomputing,2008,72(1-3):581-591.
[84]李盼池,李士勇.基于量子遗传算法的正规模糊神经网络控制器设计[J].系统仿真学报，2007,19(16)：3710-3714.
[85]Grover L K.Quantum mechanics helps in searching for a needle in a haystack[J].Physical Review Letters,1997,79(2):325-328.
[86]Grover L K.Quantum computers can search rapidly by using almost any transformation[J]. Physical Review Letters,1998,80(19):4329-4332.
[87]Biham E,Biham O,Birom D.Grover's quantum search algorithm for an arbitrary initial amplitude distribution[J].Physical Review A,1999,60(4):2742-2745.
[98]Biham E,Biham O,Biron D.Analysis of generalized Grover quantum search algorithms using recursion equations[J].Physical Review A,2000,63(1):1-8.
[89]Hoyer P.Arbitrary phases in quantum amplitude amplification[J].Physical Review A,2000, 62(5):1-5.
[90]Biham E,Kenigsberg D.Grover's quantum search algorithm for an arbitrary initial mixed state[J].Physical Review A,2002,66(6):1-4.
[91]Grover L K.Fixed-point quantum search[J].Physical Review Letters,2005,95(15):1-4.
[92]Biham D,Shapira Y.Analysis of Grover's quantum search algorithm as a dynamical system[J]. Physical Review A,2003,68(2):1-8.
[93]Long G L,Li Y S,Zhang W L.Phase matching in quantum searching[J].Physics Letters A, 1999,262(1):27-34.
[94]邵问律,吴盛俊,张永德.量子Grover算法及其在遍历搜寻中的应用[J].大学物理,2000，19(1)：1-4.
[95]Li D F,Li X X.More general quantum search algorithm and the precise formula for the amplitude and the non-syssetric effects of different rotating angles[J].Physics Letters A,2001, 287(5-6):304-316.
[96]Long G L,Li X,Sun Y.Phase matching condition for quantum search with a generalized initial state[J].Physics Letters A,2002,294(1):143-152.
[97]宋辉，戴葵，王志英.一种改进的量子搜索算法[J].计算机工程与科学，2002，24(5)：4-7,14.
[98]孙吉贵,何雨果.量子搜索算法[J].软件学报，2003，14(3)：334-344.
[99]陈洪光，李飚,沈振康.逼近全概率Grover算法的搜索次数计算[J].计算机工程与应用,2004,(3)：58-59.
[100]李映,张艳宁,赵荣椿.量子搜索和进化搜索算法的比较较研究[J].计算机工程与应用,2004，(18)：1-4.
[101]韩伟,郑宝玉.基于Grover算法的量子多用户检测[J].徐州师范大学学报(自然科学版),2005，23(1)：35-38.
[102]穆方军,游志胜,赵明华.用Grover量子搜索算法挖掘网络数据[J].计算机应用,2005,25(10)：210-2311，2317
[103]Li D F,Li X R,Huang H T.Fixed-point quantum search for different phase shifts[J].Physics Letters A,2007,326(4):260-264.
[104]孙力,须文波.量子搜索算法体系及其应用[J].计算机工程与应用，2006,(14)：55-57，75.
[105]Li P C,Li S Y.Phase matching in Grover's algorithm[J].Physics Letters A,2007,366(1-2): 42-46
[106]Li P C,Li S Y.Two improvements in Grover's algorithm[J].Chinese Journal of Electronics, 2008,17(1):100-104.
[107]Li P C,Li S Y.A Grover quantum searching algorithm based on the weighted targets[J]. Journal of Systems Engineering and Electronics,2008,19(2):363-369.
[108]李盼池,李士勇.加权量子搜索算法及其相位匹配条件研究[J].计算物理,2008，25(5)：623-630.
[109]Hornik K,Stinchcombe M,White H.Multilayer feedforward networks are universal approximators[J].Neural Networks,1989,2(5):359-366.
[110]Funahashi K.On The approximate realization of continuous mappings by neural networks[J]. Neural Networks,1989,2(3):183-192.
[111]Ou G B,Murphey Y L.Multi-class pattern classification using neural networks[J].Pattern Recognition,2007,40(1):4-18.
[112]Purwara S,Karb I N,Jhab A N.On-line system identification of complex systems using Chebushev neural networks[J].Applied Soft Computing,2007,7(1):364-372.
[113]Hussaina A J,Knowlesa A,Lisboaa P J G.Financial time series prediction using polynomial pipelined neural networks[J].Expert Systems with Applications,2008,38(3):1186-1199.
[114]Rosenblatt F.The perception:a probabilistic model for information storage and organization in the brain[J].Psychological Review,1958,(65):388-408.
[115]Widrow B.Generalization and Infdrmation Storage in Networks of Adaline 'neurons'[M]. Washington,DC:Spartan Books,1962.
[116]Kohonen T.Self-orgatlization formation of topologically correct feature maps[J].Biological Cybernetics,1982,43(1):59-69.
[117]Anderson J A.Neocognitron:a self-oraanizing neural network model for a mechanism of pattern recognition unaffected by shift in position[J].Biological Cybernetics,1980,36(4): 193-202.
[118]Grossberg S.Adaptive pattern classification and universal recodipg:Ⅰ.parallel development and coding of neural feature detectors[J].Biological Cybernetics,1976,23(3):121-134.
[119]Hopfield J J,Tank D W."Neural" computation of decisions in optimizaton problems[J]. Biological Cybernetics,1985,52(3):141-152.
[120]Rumelhart D E,Hinton G E,Williams R J.Learning representations by back-propagating errors[J].Nature,1986,323(9):533-536.
[121]欧阳楷，刘卫芳.基于生物的神经网络的理论框架—神经元模型[J].北京生物医学工程,1997，16(2)：93-101.
[122]Zhang L I,Tao H W,Holt C E.A critical window for cooperdtion and competition among developing retinotedtal synapses[J].Nature,1998,395(3):37-44
[123]Hush D R,Horne B G.Progress in supervised neural networks[J].IEEE Signal Processing Magazine,1993,10(1):8-39.
[124]Elman J L.Finding structure in time[J].Cognitive Science,1990,14(2):179-211.
[125]许少华,何新贵,,刘坤.关于连续过程神经元网络的一些理论问题[J].电子学报,2006,34(10)：1838-1841.
[126]许少华,何新贵,李盼池.自组织过程神经网络及其应用研究[J].计算机研究与发展,2003，40(11)：1612-1615.
[127]许少华,何新贵.径向基过程神经元网络及其应用研究[J].北京航空航天大学学报,2004,30(1)：14-17.
[128]许少华,何新贵.一种级联过程神经元网络及其应用研究[J].模式识别与人工智能,2004,17(2)：201-211.
[129]钟诗胜,丁刚.双并联前向过程神、经网络及其应用研究[J].控制与决策,2005,20(7)：764-768.
[130]梁久祯.分段式过程神经元网络[J].模式识别与人工智能，2006，19(3)：295-299.
[131]何新贵,许少华.一类反馈过程神经元网络模型及其学习算法[J].自动化学报,2004,30(6)：801-806
[132]许少华,何新贵.基于函数正交基展开的过程神经网络学习算法[J].计算机学报,2004,27(5)：645-650.
[133]钟诗胜,朴树学,丁刚.改进BP算法在过程神经网络中的应用[J].哈尔滨工业大学学报,2006,38(6)：840-842.
[134]许少华,何新贵.多聚合过程神经元网络及其学习算法研究[J].计算机学报，2007，30(1)：48-56.
[135]许少华,何新贵,王兵.一种时变输入输出过程神经元网络及学习算法研究[J].控制与决策，2007,22(12):1425-1428.
[136]钟诗胜,李洋.基于小波过程神经网络的飞机发动机状态监视[J].航空学报,2007,28(1)：68-71.
[137]丁刚,钟诗胜.基于时变阈值过程神经网络的太阳黑子数预测[J].物理学报，2007，56(2)：1224-1230.
[138]Zhong S S, Ding G, Su D Z. Parallel Feedforward Process Neural Network with Time-varying Input and Output Functions[C].Advances in Neural Networks-ISNN2005,Lecture Notes in Computer Science 3496, Berlin:Springer-Verlag,2005:473-478.
[139]许少华,刘扬,何新贵.基于过程神经网络的水淹层自动识别系统[J].石油学报，2004,25(4)：54-57.
[140]李盼池.用过程神经网络和遗传算实现系统逆向求解[J].控制理论与应用，2005,22(6)：895-899.
[141]丁刚,钟诗胜.基于过程神经网络的热平衡温度预测研究[J].宇航学报，2006,27(3)：489-492.
[142]Ding G, Zhong S S.Time Series Prediction by Parallel Feedforward Process Neural Network with Time-varied Input and Output Functions[J].Neural Network World,2005,15(2): 137-147.
[143]Ding G, Zhong S S. Aircraft Engine Lubricating Oil Monitoring by Process Neural Network[J].Neural Network World,2006,16(1):15-24.
[144]汤成友,官学文,张世明.现代中长期水文预报方法及应用[M].北京：中国水利水电出版社,2008.
[145]谢景新.非线性多步预测与优化方法及其在水文预报中的应用[D].大连：大连理工大学,2006.
[146]武新宇.不确定环境下水电系统多维优化理论和应用[D].大连：大连理工大学,2006.
[147]王文川.水电系统中预报与调度的混合智能方法研究及应用[D].大连：大连理工大学,2008.
[148]牛东晓，邢棉.时间序列的小波神经网络预测模型研究[J].系统工程理论与实践,1999,(5)：89-92.
[149]王文圣,袁鹏,丁晶.小波分析及其在日径流过程随机模拟中的应用[J].水利学报,2000,(11)：43-47.
[150]王文圣,丁晶,向红莲.小波分析在水文学中的应用研究及展望[J].水科学进展,2002,13(4)：515-520.
[151]陈守煜.模糊水文学[J].大连理工大学学报,1988,(3)：93-97.
[152]陈守煜.模糊水文学与水资源系统模糊优化原理[M].大连：大连理工大学出版社,1990.
[153]王本德.水文中长期预报模糊数学方法[M].大连：大连理工大学出版社,1993.
[154]陈守煜，许士国.径流分级长期预报的模糊聚类分析法[J].水利学报，1986,(7)：43-49.
[155]陈守煜.中长期水文预报综合分析理论模式与方法[J].水利学报,1997,(8)：15-21.
[156]陈守煜.水文水资源系统模糊识别理论[M].大连：大连理工大学出版社,1992.
[157]Karunanithi N,Grenney W,Whitley D,et al.Neural Networks for River Flow Prediction[J]. Journal of Computing in Civil Engineering,1994,8(2):201-220.
[158]胡铁松,袁鹏,丁晶.人工神经网络在水文水资源中的应用[J].水科学进展,1995,6(1)：76-82.
[159]蔡煜东,许伟杰.自组织人工神经网络在鄱阳湖年最高水位长期预报中的应用[J].水文科技情报,1993,10(2):27-29.
[160]钟登华,王仁超,皮钧.水文预报时间序列神经网络模型[J].水利学报,1995,(2)：69-75.
[161]Hsu K,Gupta H V,Sorroshian S.Artificial neural network modelling of the rainfall-runoff process[J]Water Resources Research 1995,31(10):2517-2530.
[162]赵永龙,丁晶,邓育仁.相空间小波网络模型及其在水文中长期预测中的应用[J].水科学进展，1998，9(3)：252-257.
[163]胡铁松,丁晶.径流长期分级预报的Kohonen网络方法[J].水电站设计,1997,13(2)：24-29.
[164]冯国章，李佩成.人工神经网络结构对径流预报精度的影响分析[J].自然资源学报,1998,13(2)：169-174.
[165]许永功，李书琴,裴金萍.径流中长期预报的人工神经网络模型的建立与应用[J].干旱地区农业研究,2001,19(3)：104-108.
[166]Huang W,Xu B,Chan-Hilton A.Forecasting flows in Apalachicola River using neural networks[J].Hydrological Processes,2004,18(13):2545-2564.
167] Breaford P W,Seyfried M S,Matison T H.Searching for chaotic dynamic in snowmelt runoff[J].Water Resources Research,1991,27(6):1005-1010.
[168]Sivakumar B,Berndtsson R,Persson M.Monthly runoff prediction using phase-space reconstruction[J].Hydrological Sciences Journal,2001,46(3):377-388.
[169]权先璋,蒋传文,张勇传.径流预报的混沌神经网络理论及应用[J].武汉城市建设学院学报，1999，16(3)：33-37.
[170]尤卫红，杞明辉,段旭.小波变换在短期气候预测模型[J].高原气象，1999，18(1)：39-46.
[171]李士勇.模糊控制·神经控制和智能控制论(第二版)[M].哈尔滨工业大学出版社，1998.
[172]Kufler W S,Nicholls J G,Martin A R.张人骥译.生物神经学[M].北京天学出版社,1991.
[173]丛爽.量子力学系统控制导论[M].科学出版社,2006.
[174]梁久祯.智能计算：若干理论问题及其应用[M].国防工业出版社，2007.
[175]许少华,肖红,廖太平.基于离散Walsh变换的过程神经元网络学习算法[J].大庆石油学院学报，2003，27(4)：58-61.
[176]赵千川.量子计算和量子信息：量子计算部分[M].清华大学出版社，2004.
[177]Perus M,Ecimovic P.Memory and pattern recognition in associative neural network[J]. International Journal of Applied Science and Computation,1998,(4):283-310.
[178]Long G L.Grover algorithm with zero theoretical failure rates[J].Physical Review A,2002, 64(2):1-4.
[179]Younes A.Fixed phase quantum search algorithm[J].quant-ph/0704.1585,2007,1-8.
[180]Younes A,Rowe J,Miller J.Quantum search algorithm with more reliable behavior using partial diffusion[J].quant-ph/03120.22,2004,1-27.
[181]许少华,何新贵，李盼池.一类用于连续过程逼近的过程神经元网络及其应用[J].信息与控制,2004,33(1)：116-119.
[182]许少华,何新贵,尚福华.基于基函数展开的双隐层过程神经元网络及其应用[J].控制与决策,2004,19(1)：36-40.
[183]焦李成,杜海峰，刘芳.免疫优化计算、学习与识别[M].科学出版社,2006.
[184]Zhang G X,Li N,Jin W D.A novel quantum genetic algorithm and its application[J].ACTA Electronics Sinica,2004,32(3):476-479.
[185]Grigorenko I,Garcia M E.Calculation of the partition function using quantum genetic algorithms[J].Physical A,2002,313(3-4):463-470.
[186]陈宗海,董道毅,张陈斌.量子控制导论[M].中国科学技术大学出版社，2005.
[187]Nasaroui Q,Gonzalez F,Dasgupta D.The fuzzy artificial immune system:Motivations,basic concepts and application to clustering and Web profiling[J].Fuzzy System,2002,1(2): 711-716.
[188]Chun J S,Jung H K,Hahn S Y.A Study on comparison of optimization performance between immune algorithm and other heuristic algorithms[J].IEEE Transactions on Magnetic,1998, 34(5):2972-2975.
[189]张彤,王宏伟,王子才.变尺度混沌优化方法及其应用[J].控制与决策,1999,14(3)：285-288.
[190]李兵，蒋慰孙.混沌优化方法及其应用[J].控制理论与应用,1997,14(4)：613-615.
[191]Dorigo M,Maniezzo V,Colorni A.Ant system:Optimization by a colony of cooperating agents[J].IEEE Transactions on System,Man,and Cybernetics,1996,26(1):29-41.
[192]Dorigo M,Gambardella L M.Ant colony System:A cooperative learning approach to the traveling salesman problem[J].IEEE Transactions on Evolutionary Computation,1997,1(1): 53-66.
[193]Colorni A,Dorigo M,Maniezzo V.Ant colony system for job-shop scheduling[J].Belgian Journal of Operations Research Statistics and Computer Science,1994,34(1):39-53.
[194]Maniezzo V.Exact and approximate non-deterministic tree search procedures for the quadratic assignment problem[J].Informs Journal of Computer,1999,11(4):358-369.
[195]Leguizamon G,Michalewicz Z.A new version of ant system for subset problems[C].Proc.of the 1999 Congress on Evolutionary Computation,Washington,DC,USA,1999:1459-1464.
[196]Wang L,Wu Q D.Ant system algorithm for optimization in continuous space[C].Proc.of the 2001 IEEE International Conference on Control Applications,Mexico City,Mexico,2001: 395-400.
[197]Jayaraman V K,Kulkarni B D,Sachin K.Ant colony framework for optimal design and scheduling of batch plants[J].Computer and Chemical Engineering,2000,24(g):1901-1912.
[198]程志刚,陈德钊，吴晓华.连续蚁群优化算法的研究[J].浙江大学学报(工学版),2005,39(8)：1147-1151.
[199]Kennedy J,Eberhart R C.Particle swarms optimization[C].Proceedings of IEEE international conference on Neural Networks,USA,1995:1942-1948.
[200]Shi X H,Liang Y C,Lee H P.Particle swarm optimization-based algorithms for TSP and generalized TSP[J].Information Processing Letters,2007,103(5):169-176.
[201]Ammar W,Nirod C,Tan K.Solving shortest path problem using particle swarm optimzation[J].Applied Soft Computing,2008,8(4):1643-1653.
[202]Lin S W,Ying K C,Chen S C.Particle swarm optimization for parameter determination and feature selection of support vector machines[J].Expert Systems with Applications,2008, 35(4):1817-1824.
[203]Marcio S,Evaristo C.Nonlinear parameter estimation through particle swarm optimization[J]. Chemical Engineering Science,2008,63(6):1542-1552.
[204]Tiago S,Arlindo S,Ana N.Particle swarm based data mining algorithms for classification tasks[J].Parallel Computing,2004,36(5-6):767-783.
[205]Falco I D,Cioppa A D,Tarantino E.Facing classification problems with particle swarm optimization[J].Applied Soft Computing,2007,7(3):652-658.
[206]Swagatam D,Ajith A,Amit K.Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm[J].Pattern Recognition Letters,2008,29(5):688-699.
[207]Jarboui B,Cheikh M,Siarry P.Combinatorial particle swarm optimization for partitional clustering problem[J].Applied Mathematics and Computation,2007,192(2):337-345.
[208]Jiang M,Luo Y P,Yang S Y.Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm[J].Information Processing Letters,2007, 102(1):8-16.
[209]Bergh F,Engelbrecht A P.A study of particle swarm optimization particle trajectories[J]. Information Science,2006,176(8):937-971.
[210]Cai X J,Cui Z H.Zeng J C.Dispersed particle swarm optimization[J].Information Processing Letters,2008,105(6):231-235.
[211]Chatterjee A,Siarry P.Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization[J].Computers & operations research,2007,33(3):859-871.
[212]Lu Z S,Hou Z R.Particle swarm optimization with adaptive mutation [J].ACTA Electronica Sinica,2004,32(3):416-420.
[213]Liu Y,Qin Z,Shi Z W.Center particle swarm optimization[J].Neurocomputing,2007, 70(4-6):672-679.
[214]Liu B,Wang L,Jin Y H.Improved particle swarm optimization combined with chaos[J]. Chaos Solitons & Fractals,2005,25(5):1261-1271.
[215]Luo Q,Yi D Y.A co-evolving framework for robust particle swarm optimization[J].Applied Mathematics and Computation,2008,199(2):611-622.
[216]Jiang Y,Hu T S,Huang C C.An improve dparticle swarm optimization algorithm[J].Applied Mathematics and Computation,2007,193(1):231-239.
[217]Li L L,Wang L,Liu L H.An effective hybrid PSOGA strategy for optimization and its application to parameter estimation[J].Applied Mathematics and Computation,2006,179(1): 135-146.
[218]Eberhart R C,Shi Y.Comparing inertia weights and constriction factors in particle swarm optimization[G].Proceedings of the IEEE Congress on Evolutionary Computation,USA,2000: 84-88.
[219]Chen H L,Rao A R.Testing hydrologic time series for stationarity[J].Journal of Hydrologic Engineering,2002,7(2):129-136.
[220]Salas J D,Analysis and modeling of hydrologic time series[M].1993.
[221]Srikanthan R,McMahon T A.Stochastic generation of annual,monthly and daily climate data: A review[J].Hydrology and Earth System Sciences,2001,5:(4):653-670.
[222]Committee A T.Artificial neural networks in hydrology-Ⅰ:Preliminary concepts[J].Journal of Hydrologic Engineering,2000,5(2):115-123.
[223]Committee A T.Artificial neural networks in hydrology-Ⅱ:Hydrological applications[J]. Journal of Hydrologic Engineering,2000; 5(2):124-137.
[224]Sudheer K P,Gosain A K,Ramasastri K S.A data-driven algorithm for constructing artificial neural network rainfall-runoff models[J].Hydrological Processes,2002; 16(6):1325-1330.
[225]Lin J Y,Cheng C,T,Chau K W.Using support vector machihes for long-term discharge prediction[J].Hydrological Sciences Journal-Journal Des Sciences Hydrologiques,2006, 51:(4):599-612.
[226]Santhi C,Arnold J G,Williams J R,et al.Validation of the swat model on a large river basin with point and nonpoint sources[J].Journal of the American Water Resources Association, 2001,37(5):1169-1188.
[227]Van Liew M W,Arnold J G,Garbrecht J D.Hydrologic simulation on agricultural watersheds: Choosing between two models[J].Transactions of the Asae,2003,46(6):1539-1551.
[228]Nash J E,Sutcliffe J V.River flow forecasting through conceptual models part Ⅰ- A discussion of principles[J].Journal of Hydrology,1970,10(3):282-290.
[229]Moriasi D N,Arnold J G,Van Lievx M W,et al.Model evaluation guidelines for systematic quantification of accuracy in watershed simulations[J].Transactions of the ASABE,2007, 50(3):885-900.
[2]Grover L K.A fast quantum mechanical algorithm for database search[C].Proc.of the 28th Annual ACM Symp.on Theory of Computing,New York,USA,1996;212-219.
[3]Wang Z G,Wong Y S,Rahman M.Development of a parallel optimization method based on genetic simulated annealing algorithm[J].Parallel Computing,2005,31(8-9):839-857.
[4]Suman B.Study of simulated annealing based algorithms for multi objective optimization of a constrained problem[J].Computers & Chemical Engineering,2004,28(9):1849-1871.
[S]Soliman S A,Mantaway A H,El-Hawary M E.Simulated annealing optimization algorithm for power systems quality analysis[J].International Journal of Electrical Power & Energy Sysems,2004,26(1):31-36.
[6]Torrecilla J S,Otero L,Sanz P D.Optimization of an artificial neural network for thermal/pressure food processing:Evaluation of training algorithms[J].Computers and Electronics in Agriculture,2007,56(2):101-110.
[7]Sexton R S,McMurtrey S,Cleavenger D.Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem[J].European Journal of Operational Research,2006; 168(3):1009-1018.
[8]Sexton R S,Dorsey R E,Sikander N A.Simultaneous optimization of neural network function and architecture algorithm[J].Decision Support Systems,2004,36(3):283-296.
[9]Zhang Z H.Immune optimization algorithm for constrained nonlinear multiobjective optimization problems[J].Applied Soft Computing,2007,7(3):840-857.
[10]Zhang Z H.Constrained multiobjective optimization immune algorithm:Convergence and application[J].Computers & Mathematics with Applications,2006,52(5):791-808.
[11]Guo Z L,Wang S A,Zhuang J.A novel immune evolutionary algorithm incorporating chaos optimization[J].Pattern Recognition Letters,2006,27(1):2-8.
[12]Marseguerra M,Zio E,Cadini F.Genetic algorithm optimization of a model-free fuzzy control system[J].Annals of Nuclear Energy,2005,32(7):712-728.
[13]Fantinutto R,Guglieri G,Quagliotti F B.Flight control system design and optimization with a genetic algorithm[J].Aerospace Science and Technology,2005,9(1):73-80.
[14]Lewin D R,Parag A.A constrained genetic algorithm for decentralized control system structure selection and optimization[J].Automatica,2003,39(10):1801-1807.
[15]Martinez C G,Cordon O,Herrera F.A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP[J].European Journal of Operational Research,2007,180(1):116-148.
[16]Demirel N C,Toksari M D.Optimization of the quadratic assignment problem using an ant colony algorithm[J].Applied Mathematics and Computation,2006,183(1):427-435.
[17]Dreo J,Siarry P.An ant colony algorithm aimed at dynamic continuous optimization[J]. Applied Mathematics and Computation,2006,181(1):457-467.
[18]Duan H B,Wang D B,Yu X F.Novel approach to nonlinear PID parameter optimization using ant colon optimization algorithm[J].Journal of Bionic Engineering,2006,3(2):73-78.
[19]Zhang J R,Zhang J,Lok T M.A hybrid particle swarm optimization back-propagation algorithm for feedforward neural network training[J].Applied Mathematics and Computation, 2007,185(2):1026-1037.
[20]Lian Z G,Jiao B,Gu X S.A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan[J].Applied Mathematics and Computation,2006，183(2): 1008-1017.
[21]Zhao B,Guo C X,Bai B R.An improved particle swarm optimization algorithm for unit commitment[J].International Journal of Electrical Power & Energy Systems,2006,28(7): 482-490.
[22]Yin P Y,Yu S S,Wang P.A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems[J].Computer Standards & Interfaces,2006,28(4):441-450.
[23]Narayanan A,Moore M.Quantum-inspired Genetic Algorithms[C].Proceedings of IEEE International Conference on Evolutionan Computation,Nagoya,Japan,1996:61-66.
[24]Han K H,Kim J H.Genetic quantum algorithm and its application to combinational optimization problem[C].Proceedings of the International Congress on Evolutionary Computation,2000:1354-1360.
[25]Han K H,Kim J H.Analysis of quantum-inspired evolutionary algorithm[C].Proceedings of the International Congress on Artificial Intelligence,2001:727-730.
[26]Sepctor L,Barnum H,Bernstein H J.Finding a better than classical quantum AND/OR algorithm using genetic programming[C].Proceedings of the 1999 International Congress on Evolutionary Computation,1999:2239-2246.
[27]李承祖.量子信息和量子计算[M].国防科技大学出版社，2000.
[28]夏培肃.量子计算[J].计算机研究与发展，2001，38(10)：1153-1171.
[29]李士勇，李盼池.量子计算与量子智能优化算法[M].哈尔滨工业大学出版社，2009.
[30]Bshouty N,Jackson J.Learning DNF over the uniform distribution using a quantum ekample orcle[C].Proc.of the 8th Ann.Conf.Computional Learning Theory,New York,1995.
[31]Farhi E,Gutmann S.Quantum computation and decision trees[J].Physical Review A,1998, 58(2):915-928.
[32]He X G,Liang J Z.Procedure Neural Networks[C].The 16th World Computer Congress 2000, Proceedings of the Conference on Intelligent Information Processing,Beijing,2000:143-146.
[33]何新贵,梁久祯.过程神经元网络的若干理论问题[J].中国工程科学，2000，2(12)：40-44.
[34]Benioff P.Quantum mechanical hamiltonan models of Turing machines[J].Journal of Statistical Physics,1982,29(3):515-546.
[35]Kak S C.On quantum neural computing[J].Information Science,1995,83,(3-4):143-160.
[36]Toth G.Quantum cellular neural networks[J].Supertattices and Microstructures,1996,20(4): 473-478.
[37]Gopathy P,Nicolaos B.Quantum neural networks (QNNs) inherently fuzz feedforward neural networks[J].IEEE Transactions on Neural Networks,1997,8(3):6179-693.
[38]Lagaris I E.Likas A,Fotiadis D I.Artificial neural network methods in quantum mechanics[J]. Computer Physics Communications,1997,104(1-3):1-14.
[39]Ventura D,Tony M.Quantum associative memory with exponential capacity[C].Proceedings of the International Joint Conference on Neural Networks,Anchorage,Alaska,1998:509-513.
[40]Veritura D,Tony M.Quantum associative memory[J].Information Science,2000,124(1-4): 273-296.
[41]Li W G.A study of parallel neural networks[C].Proceedings of the Internatignal Joint Conference on Neural Networks,1999:1113-1116.
[42]Behrman E C,Steck J E,Skinner S R.A spatial quantum neural computer[C].Proceedings of the International Joint Conference on Neural Networks,1999:874-877.
[43]Ajit N,Tammy M.Quantum artificial neural network architectures and components[J]. Information Science,2000,128(3-4):231-255.
[44]Matsui N,Kouda N,Nishimura H.Neural network based on QBP and its performance[C]. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000:247-252.
[45]Altaisky M V.Quantum neural network[J].quart-ph/0107012,2001,1-4.
[46]Kouda N,Matsui N,Nislimura H.Qubit neural network and its learning efficiency[J].Neural Computing & Applications,2005,14(2):114-121.
[47]Maeda M,Suenaga M,Miyajima H.Qubit neuron according to quantum circuit for XOR problem[J].Applied Mathematics and Computation,2007,185(2):1015-1025.
[48]Shafee F.Neural networks with quantum gated nodes[J].Engineering Applications of Artificial Intelligence,2007,20(4):429-437.
[49]解光军,庄镇泉.量子神经网络[J].计算机科学,2001,28(7)：1-6.
[50]解光军.量子神经计算[J].合肥工业大学学报(自然科学版)，2002，25(3)：355-359.
[51]解光军，庄镇泉.量子神经计算模型研究[J].电路与系统学报，2002，7(2)：83-88.
[52]解光军，杨俊安,庄镇泉.基于量子双缝干涉实验的神经网络模型[J].模式识别与人工智能，2003，16(1)：28-32.
[53]解光军.李斌，庄镇泉.量子系统实现神经计算的理论分析[J].电子与信息学报,2003,25(5)：606-611.
[54]解光军，周典，范海秋.基于量子门组单元的神经网络及其应用[J].系统工程理论与实践,2005，(5)：113-117.
[55]解光军.量子神经计算技术[J].吉首大学学报(自然科学版),2005,26(1):3-7.
[56]钟艳花，余永权，余晓敏.量子神经动力学分析[J].量子电子学报，2005，22(2)：192-195.
[57]李飞,赵生妹，郑宝玉.量子神经网络及其在CDMA多用户检测中的应用[J].信号处理,2005,21(6)：555-559.
[58]朱大奇，桑庆兵.光电雷达电子部件的量子神经网络故障诊断算法[J].电子学报，2006,34(3)：573-576.
[59]李盼池,李士勇.一种量子自仴织特征映射网络模型及聚类算法[J].量子电子学报,2007,24(4)：463-468.
[60]吕强，俞金寿.量子神经网络软测量模型及应用[J].系统仿真学报，2007，19(24)：5696-5699.
[61]Li P C,Li S Y.Learning algorithm and application of quantum BP neural networks based on universal quantum gates[J].Journal of Systems Engineering and Electronics,2008,19(1): 167-174.
[62]周日贵,姜楠，丁秋林.量子Hopfield神经网络及图像识别[J].中国图象图形学报,2068,13(1)：119-123.
[63]李盼池.一种量子神经网络模型学习算法及应用[J].控制制理论与应用，2009，26(5)：531-534.
[64]李昐池.量子计算及其在智能优化与控制中的应用[D].哈尔滨：哈尔滨工业大学，2009.
[65]李云红,谭阳红.基于小波包与量子神经网络的容差模拟电路的软故障诊断[J].微电子学与计算机,2009,26(5):187-192.
[66]Han K H,Park K H,Lee C H.Parallel quantum-inspired genetic algorithm for combinatorial optimization problem[C].Proceedings of the International Congress on Evolutionary Computation,2001:1422-1429.
[67]Han K H,Kim J H.Quantum-inspired evolutionary algorithm for a class of combinatorial optimization [J].IEEE Transactions on Evolutionary Computation,2002,6(6):580-593.
[68]Talbi H,Draa A,Batouche M.A new quantum-inspired genetic algorithm for solving the travelling salesman problem[C].Proceedings of the International Conference on Industrial Technology,2004:1192-197.
[69]Khorsand A R,Akbarzadeh M R.Quantum gate optimization in a meta-level genetic quantum algorithm[C].2005 IEEE International Conference on Systems,Man and Cybernetics,2005: 3055-3062.
[70]Moore P,Venavagamoorthy G K.Evolving combinational logic circuits using a hybrid quantum evolution and particle swarm inspired algorithm[C].Proceedings of the NASA/DoD Conference on Evolvable Hardware,2005:97-102.
[71]Mikki S M,Kishk A A.Quantum particle swarm optimization for electromagretics[J].IEEE Transactions on Antennas and Propagation,2006,54(10):2764-2775.
[72]Zhang G X,Jin W D,Hu L Z.A novel parallel quantum genetic algorithm[C].Proceedings of the 4th International Conference on Parallel and Distributed Computing,Applications and Technologies,2003:693-697.
[73]Yang J.A,Li B,Zhuang Q.Multi-universe parallel quantum genetic algorithm and its application to blind-source separation[C].Proceedugs of the International Conference on Neural Networks and Signal Processing,2003:393-398.
[74]Chen H,Zhang J H,Zhang C.Chaos updating rotated gates quantum-inspired genetic algorithm[C].Proceedings of the International Conference on Communications,Cuircuits and Systems,2004:1108-1112.
[75]Yang S Y,Wang M,Jiao L C.A genetic algorithm based on quantum chromosome[C]. Proceedings of the 7th International Conference on Signal Processing,2004:1622-1625.
[76]Wang L,Tang F,Wu H.Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation[J].Applied Mathematics and Computation,2005, 171(2):1141-1156.
[77]王凌,刘波.微粒群优化与调度算法[M].清华大学出版社,2008.
[78]李士勇,李盼池.基于实数编码和目标函数梯度的量子遗传算法[J].哈尔滨工业大学学报，2006，38(8)：1216-1218，1223.
[79]Li P C,Li S Y.Quantum-inspired evolutionary algorithm for continuous spaces optimization[J].Chinese Journal of Electronics,2008;17(1):80-84.
[80]李盼池,李士勇.求解连续空间优化问题的量子蚁群算法[J].控制理论与应用,2008,25(2)：237-241.
[81]李盼池，李士勇.求解连续空间优化问题的混量子免疫算法[J].模式识别与人工智能,2007,20(5)：654-660.
[82]李士勇，李盼池.求解连续空间优化向题的量子粒子群算法[J].量子电子学报,2007,24(5)：569-574.
[83]Li P C,Li S Y.Quantum-inspired evolutionary algorithm for continuous spaces optimization based on Bloch coordinates of gubits[J].Neurocomputing,2008,72(1-3):581-591.
[84]李盼池,李士勇.基于量子遗传算法的正规模糊神经网络控制器设计[J].系统仿真学报，2007,19(16)：3710-3714.
[85]Grover L K.Quantum mechanics helps in searching for a needle in a haystack[J].Physical Review Letters,1997,79(2):325-328.
[86]Grover L K.Quantum computers can search rapidly by using almost any transformation[J]. Physical Review Letters,1998,80(19):4329-4332.
[87]Biham E,Biham O,Birom D.Grover's quantum search algorithm for an arbitrary initial amplitude distribution[J].Physical Review A,1999,60(4):2742-2745.
[98]Biham E,Biham O,Biron D.Analysis of generalized Grover quantum search algorithms using recursion equations[J].Physical Review A,2000,63(1):1-8.
[89]Hoyer P.Arbitrary phases in quantum amplitude amplification[J].Physical Review A,2000, 62(5):1-5.
[90]Biham E,Kenigsberg D.Grover's quantum search algorithm for an arbitrary initial mixed state[J].Physical Review A,2002,66(6):1-4.
[91]Grover L K.Fixed-point quantum search[J].Physical Review Letters,2005,95(15):1-4.
[92]Biham D,Shapira Y.Analysis of Grover's quantum search algorithm as a dynamical system[J]. Physical Review A,2003,68(2):1-8.
[93]Long G L,Li Y S,Zhang W L.Phase matching in quantum searching[J].Physics Letters A, 1999,262(1):27-34.
[94]邵问律,吴盛俊,张永德.量子Grover算法及其在遍历搜寻中的应用[J].大学物理,2000，19(1)：1-4.
[95]Li D F,Li X X.More general quantum search algorithm and the precise formula for the amplitude and the non-syssetric effects of different rotating angles[J].Physics Letters A,2001, 287(5-6):304-316.
[96]Long G L,Li X,Sun Y.Phase matching condition for quantum search with a generalized initial state[J].Physics Letters A,2002,294(1):143-152.
[97]宋辉，戴葵，王志英.一种改进的量子搜索算法[J].计算机工程与科学，2002，24(5)：4-7,14.
[98]孙吉贵,何雨果.量子搜索算法[J].软件学报，2003，14(3)：334-344.
[99]陈洪光，李飚,沈振康.逼近全概率Grover算法的搜索次数计算[J].计算机工程与应用,2004,(3)：58-59.
[100]李映,张艳宁,赵荣椿.量子搜索和进化搜索算法的比较较研究[J].计算机工程与应用,2004，(18)：1-4.
[101]韩伟,郑宝玉.基于Grover算法的量子多用户检测[J].徐州师范大学学报(自然科学版),2005，23(1)：35-38.
[102]穆方军,游志胜,赵明华.用Grover量子搜索算法挖掘网络数据[J].计算机应用,2005,25(10)：210-2311，2317
[103]Li D F,Li X R,Huang H T.Fixed-point quantum search for different phase shifts[J].Physics Letters A,2007,326(4):260-264.
[104]孙力,须文波.量子搜索算法体系及其应用[J].计算机工程与应用，2006,(14)：55-57，75.
[105]Li P C,Li S Y.Phase matching in Grover's algorithm[J].Physics Letters A,2007,366(1-2): 42-46
[106]Li P C,Li S Y.Two improvements in Grover's algorithm[J].Chinese Journal of Electronics, 2008,17(1):100-104.
[107]Li P C,Li S Y.A Grover quantum searching algorithm based on the weighted targets[J]. Journal of Systems Engineering and Electronics,2008,19(2):363-369.
[108]李盼池,李士勇.加权量子搜索算法及其相位匹配条件研究[J].计算物理,2008，25(5)：623-630.
[109]Hornik K,Stinchcombe M,White H.Multilayer feedforward networks are universal approximators[J].Neural Networks,1989,2(5):359-366.
[110]Funahashi K.On The approximate realization of continuous mappings by neural networks[J]. Neural Networks,1989,2(3):183-192.
[111]Ou G B,Murphey Y L.Multi-class pattern classification using neural networks[J].Pattern Recognition,2007,40(1):4-18.
[112]Purwara S,Karb I N,Jhab A N.On-line system identification of complex systems using Chebushev neural networks[J].Applied Soft Computing,2007,7(1):364-372.
[113]Hussaina A J,Knowlesa A,Lisboaa P J G.Financial time series prediction using polynomial pipelined neural networks[J].Expert Systems with Applications,2008,38(3):1186-1199.
[114]Rosenblatt F.The perception:a probabilistic model for information storage and organization in the brain[J].Psychological Review,1958,(65):388-408.
[115]Widrow B.Generalization and Infdrmation Storage in Networks of Adaline 'neurons'[M]. Washington,DC:Spartan Books,1962.
[116]Kohonen T.Self-orgatlization formation of topologically correct feature maps[J].Biological Cybernetics,1982,43(1):59-69.
[117]Anderson J A.Neocognitron:a self-oraanizing neural network model for a mechanism of pattern recognition unaffected by shift in position[J].Biological Cybernetics,1980,36(4): 193-202.
[118]Grossberg S.Adaptive pattern classification and universal recodipg:Ⅰ.parallel development and coding of neural feature detectors[J].Biological Cybernetics,1976,23(3):121-134.
[119]Hopfield J J,Tank D W."Neural" computation of decisions in optimizaton problems[J]. Biological Cybernetics,1985,52(3):141-152.
[120]Rumelhart D E,Hinton G E,Williams R J.Learning representations by back-propagating errors[J].Nature,1986,323(9):533-536.
[121]欧阳楷，刘卫芳.基于生物的神经网络的理论框架—神经元模型[J].北京生物医学工程,1997，16(2)：93-101.
[122]Zhang L I,Tao H W,Holt C E.A critical window for cooperdtion and competition among developing retinotedtal synapses[J].Nature,1998,395(3):37-44
[123]Hush D R,Horne B G.Progress in supervised neural networks[J].IEEE Signal Processing Magazine,1993,10(1):8-39.
[124]Elman J L.Finding structure in time[J].Cognitive Science,1990,14(2):179-211.
[125]许少华,何新贵,,刘坤.关于连续过程神经元网络的一些理论问题[J].电子学报,2006,34(10)：1838-1841.
[126]许少华,何新贵,李盼池.自组织过程神经网络及其应用研究[J].计算机研究与发展,2003，40(11)：1612-1615.
[127]许少华,何新贵.径向基过程神经元网络及其应用研究[J].北京航空航天大学学报,2004,30(1)：14-17.
[128]许少华,何新贵.一种级联过程神经元网络及其应用研究[J].模式识别与人工智能,2004,17(2)：201-211.
[129]钟诗胜,丁刚.双并联前向过程神、经网络及其应用研究[J].控制与决策,2005,20(7)：764-768.
[130]梁久祯.分段式过程神经元网络[J].模式识别与人工智能，2006，19(3)：295-299.
[131]何新贵,许少华.一类反馈过程神经元网络模型及其学习算法[J].自动化学报,2004,30(6)：801-806
[132]许少华,何新贵.基于函数正交基展开的过程神经网络学习算法[J].计算机学报,2004,27(5)：645-650.
[133]钟诗胜,朴树学,丁刚.改进BP算法在过程神经网络中的应用[J].哈尔滨工业大学学报,2006,38(6)：840-842.
[134]许少华,何新贵.多聚合过程神经元网络及其学习算法研究[J].计算机学报，2007，30(1)：48-56.
[135]许少华,何新贵,王兵.一种时变输入输出过程神经元网络及学习算法研究[J].控制与决策，2007,22(12):1425-1428.
[136]钟诗胜,李洋.基于小波过程神经网络的飞机发动机状态监视[J].航空学报,2007,28(1)：68-71.
[137]丁刚,钟诗胜.基于时变阈值过程神经网络的太阳黑子数预测[J].物理学报，2007，56(2)：1224-1230.
[138]Zhong S S, Ding G, Su D Z. Parallel Feedforward Process Neural Network with Time-varying Input and Output Functions[C].Advances in Neural Networks-ISNN2005,Lecture Notes in Computer Science 3496, Berlin:Springer-Verlag,2005:473-478.
[139]许少华,刘扬,何新贵.基于过程神经网络的水淹层自动识别系统[J].石油学报，2004,25(4)：54-57.
[140]李盼池.用过程神经网络和遗传算实现系统逆向求解[J].控制理论与应用，2005,22(6)：895-899.
[141]丁刚,钟诗胜.基于过程神经网络的热平衡温度预测研究[J].宇航学报，2006,27(3)：489-492.
[142]Ding G, Zhong S S.Time Series Prediction by Parallel Feedforward Process Neural Network with Time-varied Input and Output Functions[J].Neural Network World,2005,15(2): 137-147.
[143]Ding G, Zhong S S. Aircraft Engine Lubricating Oil Monitoring by Process Neural Network[J].Neural Network World,2006,16(1):15-24.
[144]汤成友,官学文,张世明.现代中长期水文预报方法及应用[M].北京：中国水利水电出版社,2008.
[145]谢景新.非线性多步预测与优化方法及其在水文预报中的应用[D].大连：大连理工大学,2006.
[146]武新宇.不确定环境下水电系统多维优化理论和应用[D].大连：大连理工大学,2006.
[147]王文川.水电系统中预报与调度的混合智能方法研究及应用[D].大连：大连理工大学,2008.
[148]牛东晓，邢棉.时间序列的小波神经网络预测模型研究[J].系统工程理论与实践,1999,(5)：89-92.
[149]王文圣,袁鹏,丁晶.小波分析及其在日径流过程随机模拟中的应用[J].水利学报,2000,(11)：43-47.
[150]王文圣,丁晶,向红莲.小波分析在水文学中的应用研究及展望[J].水科学进展,2002,13(4)：515-520.
[151]陈守煜.模糊水文学[J].大连理工大学学报,1988,(3)：93-97.
[152]陈守煜.模糊水文学与水资源系统模糊优化原理[M].大连：大连理工大学出版社,1990.
[153]王本德.水文中长期预报模糊数学方法[M].大连：大连理工大学出版社,1993.
[154]陈守煜，许士国.径流分级长期预报的模糊聚类分析法[J].水利学报，1986,(7)：43-49.
[155]陈守煜.中长期水文预报综合分析理论模式与方法[J].水利学报,1997,(8)：15-21.
[156]陈守煜.水文水资源系统模糊识别理论[M].大连：大连理工大学出版社,1992.
[157]Karunanithi N,Grenney W,Whitley D,et al.Neural Networks for River Flow Prediction[J]. Journal of Computing in Civil Engineering,1994,8(2):201-220.
[158]胡铁松,袁鹏,丁晶.人工神经网络在水文水资源中的应用[J].水科学进展,1995,6(1)：76-82.
[159]蔡煜东,许伟杰.自组织人工神经网络在鄱阳湖年最高水位长期预报中的应用[J].水文科技情报,1993,10(2):27-29.
[160]钟登华,王仁超,皮钧.水文预报时间序列神经网络模型[J].水利学报,1995,(2)：69-75.
[161]Hsu K,Gupta H V,Sorroshian S.Artificial neural network modelling of the rainfall-runoff process[J]Water Resources Research 1995,31(10):2517-2530.
[162]赵永龙,丁晶,邓育仁.相空间小波网络模型及其在水文中长期预测中的应用[J].水科学进展，1998，9(3)：252-257.
[163]胡铁松,丁晶.径流长期分级预报的Kohonen网络方法[J].水电站设计,1997,13(2)：24-29.
[164]冯国章，李佩成.人工神经网络结构对径流预报精度的影响分析[J].自然资源学报,1998,13(2)：169-174.
[165]许永功，李书琴,裴金萍.径流中长期预报的人工神经网络模型的建立与应用[J].干旱地区农业研究,2001,19(3)：104-108.
[166]Huang W,Xu B,Chan-Hilton A.Forecasting flows in Apalachicola River using neural networks[J].Hydrological Processes,2004,18(13):2545-2564.
167] Breaford P W,Seyfried M S,Matison T H.Searching for chaotic dynamic in snowmelt runoff[J].Water Resources Research,1991,27(6):1005-1010.
[168]Sivakumar B,Berndtsson R,Persson M.Monthly runoff prediction using phase-space reconstruction[J].Hydrological Sciences Journal,2001,46(3):377-388.
[169]权先璋,蒋传文,张勇传.径流预报的混沌神经网络理论及应用[J].武汉城市建设学院学报，1999，16(3)：33-37.
[170]尤卫红，杞明辉,段旭.小波变换在短期气候预测模型[J].高原气象，1999，18(1)：39-46.
[171]李士勇.模糊控制·神经控制和智能控制论(第二版)[M].哈尔滨工业大学出版社，1998.
[172]Kufler W S,Nicholls J G,Martin A R.张人骥译.生物神经学[M].北京天学出版社,1991.
[173]丛爽.量子力学系统控制导论[M].科学出版社,2006.
[174]梁久祯.智能计算：若干理论问题及其应用[M].国防工业出版社，2007.
[175]许少华,肖红,廖太平.基于离散Walsh变换的过程神经元网络学习算法[J].大庆石油学院学报，2003，27(4)：58-61.
[176]赵千川.量子计算和量子信息：量子计算部分[M].清华大学出版社，2004.
[177]Perus M,Ecimovic P.Memory and pattern recognition in associative neural network[J]. International Journal of Applied Science and Computation,1998,(4):283-310.
[178]Long G L.Grover algorithm with zero theoretical failure rates[J].Physical Review A,2002, 64(2):1-4.
[179]Younes A.Fixed phase quantum search algorithm[J].quant-ph/0704.1585,2007,1-8.
[180]Younes A,Rowe J,Miller J.Quantum search algorithm with more reliable behavior using partial diffusion[J].quant-ph/03120.22,2004,1-27.
[181]许少华,何新贵，李盼池.一类用于连续过程逼近的过程神经元网络及其应用[J].信息与控制,2004,33(1)：116-119.
[182]许少华,何新贵,尚福华.基于基函数展开的双隐层过程神经元网络及其应用[J].控制与决策,2004,19(1)：36-40.
[183]焦李成,杜海峰，刘芳.免疫优化计算、学习与识别[M].科学出版社,2006.
[184]Zhang G X,Li N,Jin W D.A novel quantum genetic algorithm and its application[J].ACTA Electronics Sinica,2004,32(3):476-479.
[185]Grigorenko I,Garcia M E.Calculation of the partition function using quantum genetic algorithms[J].Physical A,2002,313(3-4):463-470.
[186]陈宗海,董道毅,张陈斌.量子控制导论[M].中国科学技术大学出版社，2005.
[187]Nasaroui Q,Gonzalez F,Dasgupta D.The fuzzy artificial immune system:Motivations,basic concepts and application to clustering and Web profiling[J].Fuzzy System,2002,1(2): 711-716.
[188]Chun J S,Jung H K,Hahn S Y.A Study on comparison of optimization performance between immune algorithm and other heuristic algorithms[J].IEEE Transactions on Magnetic,1998, 34(5):2972-2975.
[189]张彤,王宏伟,王子才.变尺度混沌优化方法及其应用[J].控制与决策,1999,14(3)：285-288.
[190]李兵，蒋慰孙.混沌优化方法及其应用[J].控制理论与应用,1997,14(4)：613-615.
[191]Dorigo M,Maniezzo V,Colorni A.Ant system:Optimization by a colony of cooperating agents[J].IEEE Transactions on System,Man,and Cybernetics,1996,26(1):29-41.
[192]Dorigo M,Gambardella L M.Ant colony System:A cooperative learning approach to the traveling salesman problem[J].IEEE Transactions on Evolutionary Computation,1997,1(1): 53-66.
[193]Colorni A,Dorigo M,Maniezzo V.Ant colony system for job-shop scheduling[J].Belgian Journal of Operations Research Statistics and Computer Science,1994,34(1):39-53.
[194]Maniezzo V.Exact and approximate non-deterministic tree search procedures for the quadratic assignment problem[J].Informs Journal of Computer,1999,11(4):358-369.
[195]Leguizamon G,Michalewicz Z.A new version of ant system for subset problems[C].Proc.of the 1999 Congress on Evolutionary Computation,Washington,DC,USA,1999:1459-1464.
[196]Wang L,Wu Q D.Ant system algorithm for optimization in continuous space[C].Proc.of the 2001 IEEE International Conference on Control Applications,Mexico City,Mexico,2001: 395-400.
[197]Jayaraman V K,Kulkarni B D,Sachin K.Ant colony framework for optimal design and scheduling of batch plants[J].Computer and Chemical Engineering,2000,24(g):1901-1912.
[198]程志刚,陈德钊，吴晓华.连续蚁群优化算法的研究[J].浙江大学学报(工学版),2005,39(8)：1147-1151.
[199]Kennedy J,Eberhart R C.Particle swarms optimization[C].Proceedings of IEEE international conference on Neural Networks,USA,1995:1942-1948.
[200]Shi X H,Liang Y C,Lee H P.Particle swarm optimization-based algorithms for TSP and generalized TSP[J].Information Processing Letters,2007,103(5):169-176.
[201]Ammar W,Nirod C,Tan K.Solving shortest path problem using particle swarm optimzation[J].Applied Soft Computing,2008,8(4):1643-1653.
[202]Lin S W,Ying K C,Chen S C.Particle swarm optimization for parameter determination and feature selection of support vector machines[J].Expert Systems with Applications,2008, 35(4):1817-1824.
[203]Marcio S,Evaristo C.Nonlinear parameter estimation through particle swarm optimization[J]. Chemical Engineering Science,2008,63(6):1542-1552.
[204]Tiago S,Arlindo S,Ana N.Particle swarm based data mining algorithms for classification tasks[J].Parallel Computing,2004,36(5-6):767-783.
[205]Falco I D,Cioppa A D,Tarantino E.Facing classification problems with particle swarm optimization[J].Applied Soft Computing,2007,7(3):652-658.
[206]Swagatam D,Ajith A,Amit K.Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm[J].Pattern Recognition Letters,2008,29(5):688-699.
[207]Jarboui B,Cheikh M,Siarry P.Combinatorial particle swarm optimization for partitional clustering problem[J].Applied Mathematics and Computation,2007,192(2):337-345.
[208]Jiang M,Luo Y P,Yang S Y.Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm[J].Information Processing Letters,2007, 102(1):8-16.
[209]Bergh F,Engelbrecht A P.A study of particle swarm optimization particle trajectories[J]. Information Science,2006,176(8):937-971.
[210]Cai X J,Cui Z H.Zeng J C.Dispersed particle swarm optimization[J].Information Processing Letters,2008,105(6):231-235.
[211]Chatterjee A,Siarry P.Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization[J].Computers & operations research,2007,33(3):859-871.
[212]Lu Z S,Hou Z R.Particle swarm optimization with adaptive mutation [J].ACTA Electronica Sinica,2004,32(3):416-420.
[213]Liu Y,Qin Z,Shi Z W.Center particle swarm optimization[J].Neurocomputing,2007, 70(4-6):672-679.
[214]Liu B,Wang L,Jin Y H.Improved particle swarm optimization combined with chaos[J]. Chaos Solitons & Fractals,2005,25(5):1261-1271.
[215]Luo Q,Yi D Y.A co-evolving framework for robust particle swarm optimization[J].Applied Mathematics and Computation,2008,199(2):611-622.
[216]Jiang Y,Hu T S,Huang C C.An improve dparticle swarm optimization algorithm[J].Applied Mathematics and Computation,2007,193(1):231-239.
[217]Li L L,Wang L,Liu L H.An effective hybrid PSOGA strategy for optimization and its application to parameter estimation[J].Applied Mathematics and Computation,2006,179(1): 135-146.
[218]Eberhart R C,Shi Y.Comparing inertia weights and constriction factors in particle swarm optimization[G].Proceedings of the IEEE Congress on Evolutionary Computation,USA,2000: 84-88.
[219]Chen H L,Rao A R.Testing hydrologic time series for stationarity[J].Journal of Hydrologic Engineering,2002,7(2):129-136.
[220]Salas J D,Analysis and modeling of hydrologic time series[M].1993.
[221]Srikanthan R,McMahon T A.Stochastic generation of annual,monthly and daily climate data: A review[J].Hydrology and Earth System Sciences,2001,5:(4):653-670.
[222]Committee A T.Artificial neural networks in hydrology-Ⅰ:Preliminary concepts[J].Journal of Hydrologic Engineering,2000,5(2):115-123.
[223]Committee A T.Artificial neural networks in hydrology-Ⅱ:Hydrological applications[J]. Journal of Hydrologic Engineering,2000; 5(2):124-137.
[224]Sudheer K P,Gosain A K,Ramasastri K S.A data-driven algorithm for constructing artificial neural network rainfall-runoff models[J].Hydrological Processes,2002; 16(6):1325-1330.
[225]Lin J Y,Cheng C,T,Chau K W.Using support vector machihes for long-term discharge prediction[J].Hydrological Sciences Journal-Journal Des Sciences Hydrologiques,2006, 51:(4):599-612.
[226]Santhi C,Arnold J G,Williams J R,et al.Validation of the swat model on a large river basin with point and nonpoint sources[J].Journal of the American Water Resources Association, 2001,37(5):1169-1188.
[227]Van Liew M W,Arnold J G,Garbrecht J D.Hydrologic simulation on agricultural watersheds: Choosing between two models[J].Transactions of the Asae,2003,46(6):1539-1551.
[228]Nash J E,Sutcliffe J V.River flow forecasting through conceptual models part Ⅰ- A discussion of principles[J].Journal of Hydrology,1970,10(3):282-290.
[229]Moriasi D N,Arnold J G,Van Lievx M W,et al.Model evaluation guidelines for systematic quantification of accuracy in watershed simulations[J].Transactions of the ASABE,2007, 50(3):885-900.