基于免疫算法的污水处理系统预测及优化控制研究
|
摘要
污水生化处理过程机理复杂,具有强耦合性、非线性、时变性等特征,智能化算法的引入能有力推动污水处理研究的发展。免疫算法是基于生物免疫机理而建立起来的用于解决各种复杂问题的智能化算法,已经广泛应用在故障诊断、优化计算、智能控制等领域中。但是,免疫算法的理论和应用研究还有较大的空间,在污水处理系统中的应用研究还很少。本文在总结前人工作的基础上,对免疫算法的理论进行了深入的研究和改进,并将其应用于污水处理系统的异常数据检测、出水水质预测、模型参数估计、解耦控制及最优控制中。
本文的主要研究内容概括如下:
1.为了获得高品质的污水处理数据,采用改进的实值负向选择算法对采集到的数据进行检测,剔除异常数据。该算法通过采用可变尺寸检测器,同时限制检测器的最小半径来提高算法的性能。通过对时间序列信号进行检测,验证了改进算法的有效性。将改进的实值负向选择算法应用于污水处理系统异常数据检测中,仿真结果表明,该算法提高了污水异常数据的检测精度。
2.为了提高污水处理系统出水水质的预测精度,根据某污水处理厂工艺流程及进水水质特点,分析了影响出水水质的主要因素。污水处理系统是一个多输入多输出系统,而传统的支持向量回归机(SVRM)算法只适用于单输出系统,若采用构造一系列单输出SVRM模型的方法,由于输出变量之间的关联性,增加了算法复杂性且精度较差。为了解决多输出系统预测问题,提出了一种多输出最小二乘支持向量回归机(LS-SVRM)算法。采用多输出LS-SVRM进行污水处理系统出水水质预测,并应用免疫算法来优化多输出LS-SVRM的参数。仿真结果表明,所提出的方法提高了污水处理系统出水水质的预测精度。
3.针对污水处理系统活性污泥模型参数不确定,以及应用于不同环境的模型参数并不能取同样参数值的问题,提出了一种基于改进免疫算法的模型参数估计方法。在改进的免疫算法中,根据生物免疫机制及生物进化的周期性,设计了一种周期变化变异算子,提高了算法的搜索效率。同时,将抗体浓度与亲和度矢量距离相结合作为评价指标,设计了一种改进的免疫选择算子,避免了仅仅以亲和度作为免疫选择评价标准,低亲和度抗体过度抑制的缺点。基于马尔科夫链分析了改进免疫算法的收敛性,并通过测试函数验证了该算法的有效性。将改进的免疫算法应用到活性污泥模型参数估计中,仿真结果表明,该方法提高了模型参数的估计精度。
4.针对氨氮浓度和硝态氮浓度之间相互耦合,以及常规的PID控制方法难以获得满意控制效果的特点,以溶解氧浓度和内循环流量为操作变量,采用PID神经网络(PIDNN)对氨氮浓度和硝态氮浓度进行解耦控制。针对PIDNN连接权值容易陷入局部最优值,应用免疫算法优化PIDNN连接权值,并对解耦控制系统的稳定条件进行了分析。免疫算法采用抗体浓度与亲和度矢量距离相结合的免疫选择算子,提高算法寻找PIDNN连接权值最优值的能力。仿真所需的化学计量系数和动力学参数采用活性污泥模型参数估计值,仿真结果表明,该方法对污水生化处理系统具有很好的解耦能力和控制品质。
5.针对污水处理过程运行费用最优控制问题,以污泥排放量和溶解氧浓度作为控制变量,以剩余污泥处理、污泥回流与供气三者的运行费用之和作为性能指标,以有机物排放总量和出水水质作为约束条件,提出了一种新型免疫算法求解污水处理过程运行费用的最优值。新型免疫算法将变尺度方法引入到高斯变异和柯西变异中,设计了一种变尺度混合变异算子,提高了算法的搜索效率,并对该算法的收敛性、稳定性和时间复杂度进行了分析。将新型免疫算法与基本免疫算法和遗传算法进行比较,仿真结果表明,新型免疫算法的收敛速度最快,搜索到的运行费用最优值的次数最多,运行费用平均值和方差最小。
Biological wastewater treatment process is a non-linear strongly coupling time-varying system with complex mechanism. The introduction of intelligent algorithms is a strong impetus for development on wastewater treatment research. Immune algorithm is one of intelligent algorithms that solve various complex problems based on biological immune mechanism. The immune algorithm has been widely applied in fault diagnosis, optimization, and intelligent control, et al. More in-depth theory and application studies of immune algorithm should be carried out. The applications of immune algorithm in wastewater treatment system are far from well-enough. Based on previous work, we further study and improve the immune algorithm. The improved immune algorithm in the thesis is applied in anomaly data detection, effluent water quality prediction, model parameters estimation, decoupling control and optimal control of wastewater treatment system.
The main contents of the thesis are outlined as follows.
1. In order to obtain wastewater treatment data with high quality, an improved negative selection algorithm (INSA) is proposed to detect and eliminate the anomaly data. Variable-sized detectors are employed to improve the performance of INSA. At the same time, the minimum radius of detectors is limited. The effectiveness of INSA is verified through anomaly detection in time series data. Employing the INSA to detect the wastewater treatment data, simulation results show that the INSA can improve the detection precision of wastewater anomaly data.
2. In order to improve effluent water quality prediction precision of wastewater treatment system, the main factors which have influence on the effluent quality are analyzed according to the process flow and the characteristics of influent water quantity of a wastewater treatment plant. Wastewater treatment system is a multi-input multi-output system. But the traditional support vector regression machine (SVRM) algorithms are only used for single-output systems. If several SVRM models are constructed for multi-input multi-output systems, it will increase the complexity of the algorithm and the precision is poor for the correlation of output variables. In order to solve prediction problem of multi-output system, a method of multi-output least squares support vector regression machine (LS-SVRM) based on immune optimization is proposed. The multi-output LS-SVRM is used to predict effluent quality, using the immune algorithm to optimize the parameters of multi-output LS-SVRM. Simulation results show that the proposed method has a better prediction precision for wastewater treatment system.
3. Aiming at the parameters uncertainty problem of activated sludge model, a parameters estimation method based on improved immune algorithm is proposed. The model parameters used in different environments can not take the same parameter values. A periodically varying mutation operator is designed based on immune mechanism and periodical evolution of organism to improve the search ability of the improved immune algorithm. If only the affinity is taken as an immune selection evaluation criterion, low affinity antibodies will be overly inhibited. An improved immune selection operator is designed by introducing the antibody concentration to the affinity as the evaluating index. The convergence of the improved immune algorithm is analyzed based on the Markov chain. In order to test the effectiveness of the algorithm, it is applied to solve the function optimization problems. The improved immune algorithm is applied in parameters estimation of activated sludge model. Experimental results indicate that the improved immune algorithm is of high estimation precision.
4. Aiming at the characteristic of the strong coupling between ammonia nitrogen and nitrate nitrogen, and the conventional PID control method is difficult to achieve satisfactory control performance, a PID neural network (PIDNN) approach is employed to achieve decoupling control for ammonia nitrogen and nitrate nitrogen, where dissolved oxygen concentration and internal circulation flow are regarded as the control inputs. PIDNN connection weights are easy to fall into local optimum. In order to solve this problem, the immune algorithm is proposed to optimize the connection weights of PIDNN. The stability condition of the control system is analyzed. In order to improve the algorithm's ability to find optimal parameters of connection weights, the immune algorithm adopts the improved immune selection operator which introducing the antibody concentration to the affinity as the evaluating index. The stoichiometric coefficients and kinetic parameters adopt activated sludge model parameter estimation values. Simulation results show that the proposed method has a better decoupling capabilities and control quality for biological wastewater treatment system.
5. Aiming at the optimal control problem of wastewater treatment process operation cost, a novel immune algorithm is proposed to calculate the optimal value of operation cost, which takes the two most important control parameters, sludge wastage and dissolved oxygen as control variables, regards total substrate discharge and effluent water quality as restriction factors and operation cost of residual sludge treatment, sludge return and aeration as performance index. In order to improve the search efficiency, a novel scale-variable hybrid mutation operator is designed, which introduces the mutative scale method in the Gauss mutation and Cauchy mutation. The convergence, stability and time complexity of the algorithm are then analyzed, and an optimal control of operation cost is performed with the algorithm for wastewater treatment. Compared with the basic immune algorithm and genetic algorithm, experimental results indicate that the novel immune algorithm is of high search efficiency and low mean and variance of wastewater treatment operation cost.
本文的主要研究内容概括如下:
1.为了获得高品质的污水处理数据,采用改进的实值负向选择算法对采集到的数据进行检测,剔除异常数据。该算法通过采用可变尺寸检测器,同时限制检测器的最小半径来提高算法的性能。通过对时间序列信号进行检测,验证了改进算法的有效性。将改进的实值负向选择算法应用于污水处理系统异常数据检测中,仿真结果表明,该算法提高了污水异常数据的检测精度。
2.为了提高污水处理系统出水水质的预测精度,根据某污水处理厂工艺流程及进水水质特点,分析了影响出水水质的主要因素。污水处理系统是一个多输入多输出系统,而传统的支持向量回归机(SVRM)算法只适用于单输出系统,若采用构造一系列单输出SVRM模型的方法,由于输出变量之间的关联性,增加了算法复杂性且精度较差。为了解决多输出系统预测问题,提出了一种多输出最小二乘支持向量回归机(LS-SVRM)算法。采用多输出LS-SVRM进行污水处理系统出水水质预测,并应用免疫算法来优化多输出LS-SVRM的参数。仿真结果表明,所提出的方法提高了污水处理系统出水水质的预测精度。
3.针对污水处理系统活性污泥模型参数不确定,以及应用于不同环境的模型参数并不能取同样参数值的问题,提出了一种基于改进免疫算法的模型参数估计方法。在改进的免疫算法中,根据生物免疫机制及生物进化的周期性,设计了一种周期变化变异算子,提高了算法的搜索效率。同时,将抗体浓度与亲和度矢量距离相结合作为评价指标,设计了一种改进的免疫选择算子,避免了仅仅以亲和度作为免疫选择评价标准,低亲和度抗体过度抑制的缺点。基于马尔科夫链分析了改进免疫算法的收敛性,并通过测试函数验证了该算法的有效性。将改进的免疫算法应用到活性污泥模型参数估计中,仿真结果表明,该方法提高了模型参数的估计精度。
4.针对氨氮浓度和硝态氮浓度之间相互耦合,以及常规的PID控制方法难以获得满意控制效果的特点,以溶解氧浓度和内循环流量为操作变量,采用PID神经网络(PIDNN)对氨氮浓度和硝态氮浓度进行解耦控制。针对PIDNN连接权值容易陷入局部最优值,应用免疫算法优化PIDNN连接权值,并对解耦控制系统的稳定条件进行了分析。免疫算法采用抗体浓度与亲和度矢量距离相结合的免疫选择算子,提高算法寻找PIDNN连接权值最优值的能力。仿真所需的化学计量系数和动力学参数采用活性污泥模型参数估计值,仿真结果表明,该方法对污水生化处理系统具有很好的解耦能力和控制品质。
5.针对污水处理过程运行费用最优控制问题,以污泥排放量和溶解氧浓度作为控制变量,以剩余污泥处理、污泥回流与供气三者的运行费用之和作为性能指标,以有机物排放总量和出水水质作为约束条件,提出了一种新型免疫算法求解污水处理过程运行费用的最优值。新型免疫算法将变尺度方法引入到高斯变异和柯西变异中,设计了一种变尺度混合变异算子,提高了算法的搜索效率,并对该算法的收敛性、稳定性和时间复杂度进行了分析。将新型免疫算法与基本免疫算法和遗传算法进行比较,仿真结果表明,新型免疫算法的收敛速度最快,搜索到的运行费用最优值的次数最多,运行费用平均值和方差最小。
Biological wastewater treatment process is a non-linear strongly coupling time-varying system with complex mechanism. The introduction of intelligent algorithms is a strong impetus for development on wastewater treatment research. Immune algorithm is one of intelligent algorithms that solve various complex problems based on biological immune mechanism. The immune algorithm has been widely applied in fault diagnosis, optimization, and intelligent control, et al. More in-depth theory and application studies of immune algorithm should be carried out. The applications of immune algorithm in wastewater treatment system are far from well-enough. Based on previous work, we further study and improve the immune algorithm. The improved immune algorithm in the thesis is applied in anomaly data detection, effluent water quality prediction, model parameters estimation, decoupling control and optimal control of wastewater treatment system.
The main contents of the thesis are outlined as follows.
1. In order to obtain wastewater treatment data with high quality, an improved negative selection algorithm (INSA) is proposed to detect and eliminate the anomaly data. Variable-sized detectors are employed to improve the performance of INSA. At the same time, the minimum radius of detectors is limited. The effectiveness of INSA is verified through anomaly detection in time series data. Employing the INSA to detect the wastewater treatment data, simulation results show that the INSA can improve the detection precision of wastewater anomaly data.
2. In order to improve effluent water quality prediction precision of wastewater treatment system, the main factors which have influence on the effluent quality are analyzed according to the process flow and the characteristics of influent water quantity of a wastewater treatment plant. Wastewater treatment system is a multi-input multi-output system. But the traditional support vector regression machine (SVRM) algorithms are only used for single-output systems. If several SVRM models are constructed for multi-input multi-output systems, it will increase the complexity of the algorithm and the precision is poor for the correlation of output variables. In order to solve prediction problem of multi-output system, a method of multi-output least squares support vector regression machine (LS-SVRM) based on immune optimization is proposed. The multi-output LS-SVRM is used to predict effluent quality, using the immune algorithm to optimize the parameters of multi-output LS-SVRM. Simulation results show that the proposed method has a better prediction precision for wastewater treatment system.
3. Aiming at the parameters uncertainty problem of activated sludge model, a parameters estimation method based on improved immune algorithm is proposed. The model parameters used in different environments can not take the same parameter values. A periodically varying mutation operator is designed based on immune mechanism and periodical evolution of organism to improve the search ability of the improved immune algorithm. If only the affinity is taken as an immune selection evaluation criterion, low affinity antibodies will be overly inhibited. An improved immune selection operator is designed by introducing the antibody concentration to the affinity as the evaluating index. The convergence of the improved immune algorithm is analyzed based on the Markov chain. In order to test the effectiveness of the algorithm, it is applied to solve the function optimization problems. The improved immune algorithm is applied in parameters estimation of activated sludge model. Experimental results indicate that the improved immune algorithm is of high estimation precision.
4. Aiming at the characteristic of the strong coupling between ammonia nitrogen and nitrate nitrogen, and the conventional PID control method is difficult to achieve satisfactory control performance, a PID neural network (PIDNN) approach is employed to achieve decoupling control for ammonia nitrogen and nitrate nitrogen, where dissolved oxygen concentration and internal circulation flow are regarded as the control inputs. PIDNN connection weights are easy to fall into local optimum. In order to solve this problem, the immune algorithm is proposed to optimize the connection weights of PIDNN. The stability condition of the control system is analyzed. In order to improve the algorithm's ability to find optimal parameters of connection weights, the immune algorithm adopts the improved immune selection operator which introducing the antibody concentration to the affinity as the evaluating index. The stoichiometric coefficients and kinetic parameters adopt activated sludge model parameter estimation values. Simulation results show that the proposed method has a better decoupling capabilities and control quality for biological wastewater treatment system.
5. Aiming at the optimal control problem of wastewater treatment process operation cost, a novel immune algorithm is proposed to calculate the optimal value of operation cost, which takes the two most important control parameters, sludge wastage and dissolved oxygen as control variables, regards total substrate discharge and effluent water quality as restriction factors and operation cost of residual sludge treatment, sludge return and aeration as performance index. In order to improve the search efficiency, a novel scale-variable hybrid mutation operator is designed, which introduces the mutative scale method in the Gauss mutation and Cauchy mutation. The convergence, stability and time complexity of the algorithm are then analyzed, and an optimal control of operation cost is performed with the algorithm for wastewater treatment. Compared with the basic immune algorithm and genetic algorithm, experimental results indicate that the novel immune algorithm is of high search efficiency and low mean and variance of wastewater treatment operation cost.
引文
[1]司书红,朱高峰,苏永红.西北内陆河流域的水循环特征及生态学意义[J].干旱区资源与环境, 2010, 24(9): 37-44
[2]周生贤. 2009年中国环境状况公报[R].北京:中国环境保护部, 2010
[3] Zhang X. X., Zhao D. Y., Wang, Z. X., et al. Environmental biologicalmodel based on optimization of activated sludge process [J]. International Journal of Environmental Science and Technology, 2009, 6(1): 69-76
[4]徐亚同,黄民生.废水生物处理的运行管理与异常对策[M].北京:化学工业出版社, 2003: 91-92
[5]易赛莉,卢磊.城市污水处理可持续发展工艺选型和技改方法初探[J].环境科学与技术, 2007, 30(8): 60-63
[6]王华丰.基于ASM2的A2/O工艺机理模型及自动控制研究[D].杭州:浙江大学,2005
[7]高大文,彭永臻.实时控制技术在污水生物处理中的研究进展[J].应用与环境生物学报, 2004, 10(6): 821-825
[8] Albazzaz H., Wang X. Z., Marhoon F. Multidimensional visualisation for process historical data analysis-a comparative study with multivariate statistical process control [J]. Journal of Process Control, 2005, 15(3): 285-294
[9] Albazzaz H., Wang X. Z. Historical data analysis based on plots of independent and parallel coordinates and statistical control limits [J]. Journal of Process Control, 2006, 16(2): 103-114
[10] Bao H., Khan F., Iqbal T., et al. Risk-based fault diagnosis and safety management for process systems [J]. Process Safety Progress, 2011, 30(1): 6-17
[11] Hong Y. S. T., Rosen M. R., Bhamidimarri R. Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis [J]. Water Research, 2003, 37(7): 1608-1618
[12]李晓东,曾光明,蒋茹,等.改进支持向量机对污水处理厂运行状况的故障诊断[J].湖南大学学报(自然科学版), 2007, 34(12): 68-71
[13] Zeng G. M., Li X. D., Jiang R. et al. Fault Diagnosis of WWTP Based on ImprovedSupport Vector Machine [J]. Environmental Engineering Science, 2006, 23(6): 1044-1054
[14]杨红,高月芳,罗飞.基于人工免疫和ICA的污水进水水质LS-SVM分类[J].自动化与仪表, 2010, (6): 1-4
[15] Dellana S.A., West D. Predictive modeling for wastewater applications: Linear and nonlinear approaches [J]. Environmental Modelling and Software, 2009, 24(1): 96-106
[16] Akratos C.S., Papaspyros, J.N.E., Tsihrintzis V. A., et al. Artificial neural network use in ortho-phosphate and total phosphorus removal prediction in horizontal subsurface flow constructed wetlands [J]. Biosystems Engineering, 2009, 102(2):190-201
[17] Hamed M. M., Khalafallah M. G., Hassanien E. A. Prediction of wastewater treatment plant performace using artificial neural networks [J]. Environmental Modelling & Software, 2004, 19(10): 919-928
[18] Mjalli F. S., Al-Asheh S., Alfadala H. E. Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance [J]. Journal of Environmental Management, 2007, 83(3): 329-338
[19] Raduly B., Gernaey K. V., Capodaglio A. G., et al. Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study [J]. Environmental Modelling & Software, 2007, 22(8), 1208-1216
[20] Molga E., Cherbanski R., Szpyrkowicz L. Modeling of an industrial full-scale plant for biological treatment of textile wastewater: Application of neural network [J]. Industrial & Engineering Chemistry Research, 2006, 45(3) : 1039-1046
[21] Machon I., Lopez H., Rodriguez-Iglesias J., et al. Simulation of a coke wastewater nitrification process using a feed-forward neuronal net [J]. Environmental Modelling & Software, 2007, 22(9): 1382-1387
[22]韦安磊,曾光明,黄国和,等.生物脱氮除磷活性污泥系统复合模拟方法[J].环境工程学报, 2010, 4(11): 2590-2594
[23] RI S.I.,侯德刚,张振家,等.基于BP人工神经网络的生化处理水水质预测[J].现代化工, 2009, 29(12): 66-70
[24]楼文高,刘遂庆.活性污泥系统神经网络建模与仿真研究[J].环境污染与防治, 2005, 27(9): 704-707
[25]乔俊飞,韩红桂. RBF神经网络的结构动态优化设计[J].自动化学报, 2010, 36(6):865-872
[26] Hanbay D., Turkoglu I., Demir Y. Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks [J]. Expert Systems with Applications, 2008, 34(2): 1038-1043
[27] Pai T. Y. , Wan T. J., Hsu S. T., et al. Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent [J]. Computers and Chemical Engineering, 2009, 33(7): 1272-1278
[28]李明河,杨明.污水总氮软测量模型的研究与应用[J].自动化与仪表, 2009, 24(9): 1-4
[29] Luo F., Yu R.H., Xu Y.G., et al. Effluent quality prediction of wastewater treatment plant based on fuzzy-rough sets and artificial neural networks [A]. The 6th International Conference on Fuzzy Systems and Knowledge Discovery[C]. Piscataway: IEEE Computer Society, 2009: 47-51
[30]郑广勇,罗飞,陈伟斌.基于免疫粒子群神经网络的污水水质预测[J].微处理机, 2010, 31(2): 75-81
[31]韩耀宗,黄亮亮,宋新山.基于小波神经网络的芦苇潜流人工湿地水质预测[J].环境科学研究, 2009, 22(12): 1460-1465
[32]朱启兵,黄敏,崔宝同.基于小波神经网络的污水处理厂出水水质预测[J].计算机工程与应用, 2007, 43(28): 15-17
[33]丛秋梅,柴天佑,余文.带有稳定学习算法的小波神经网络及应用[J].东北大学学报(自然科学版), 2010, 31(3): 305-308
[34] Hong Y. S., Rosen M.R., Bhamidimarri R. Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis [J]. Water Research, 2003, 37(7): 1608-1618
[35] Cinar O. New tool for evaluation of performance of wastewater treatment plant: Artificial neural network [J]. Process Biochemistry. 2005, 40(9): 2980-2984
[36] Grieu S., Thiery F., Traore A., et al. KSOM and MLP neural networks for on-line estimating the efficiency of an activated sludge process [J]. Chemical Engineering Journal, 2006, 116(1): 1-11
[37] Fernandez F. J., Seco A., Ferrer J., et al. Use of neurofuzzy networks to improvewastewater flow-rate forecasting [J]. Environmental Modelling & Software, 2009, 24(6): 686-693
[38]王海云,冯裕钊,张晓清,等.一种基于补偿模糊神经网络的水质预测方法[J].重庆建筑大学学报, 2004, 26(5): 77-81
[39]冯裕钊,卓明,田利强,等.聚类方法在污水处理软测量中的应用机理研究[J].后勤工程学院学报, 2007, 23(1): 60-65
[40]余伟,罗飞,杨红,等.基于多神经网络的污水氨氮预测模型[J].华南理工大学学报(自然科学版), 2010, 38(12): 79-83
[41] Ye H.T., Luo F., Xu Y.G. Application of RBF Network Based on Immune Algorithm to Predicting of Wastewater Treatment [J]. Lecture Notes in Computer Science, 2009, 5553(3): 1197-1202
[42] Clara N. Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants [J]. WSEAS Transactions on Systems, 2008, 7(6): 695-705
[43]李峰,吴敏,杨哲.活性污泥数学模型在我国的研究进展[J].中国资源综合利用, 2007, 25(11): 21-23
[44] Henze M., Grady C. P., Gujor W., et al. Activated sludge model NO.1 [R]. London , England: Int. Association on Water Pollution Research and Control (IAWPRC), Scientific and Technical Report N0.1, 1986
[45] Henze M., Gujor W., Mino T., et al. Activated sludge model N0.2 [R]. London ,England: Int. Association on Water Pollution Research and Control (IAWPRC), Scientific and Technical Report N0.2, 1995
[46] Gujor W., Henze M., Mino T., et al. Activated sludge model N0.3 [J]. Water Science and Technology. 1999, 39(1): 183-193
[47]刘光莲.活性污泥数学模型在污水处理中的研究和应用进展[J].水科学与工程技术, 2009, (1): 31-33
[48]朱嵩,毛根海,程伟平,等.基于贝叶斯推理的水环境系统参数识别[J].江苏大学学报(自然科学版), 2007, 28(3): 237-240
[49]邓义祥.稀疏数据条件下河流水质模型的参数识别[D].北京:清华大学, 2003
[50] Rojas R., Feyen L., Dassargues A. Conceptual model uncertainty in groundwater modeling: Combining generalized likelihood uncertainty estimation and Bayesian model averaging [J]. Water Resources Research, 2008, 44(12): 1-16
[51]王建平,程声通,贾海峰.环境模型参数识别方法研究综述[J].水科学进展, 2006, 17(4): 574-580
[52]王薇,曾光明,何理.用模拟退火算法估计水质模型参数[J].水利学报, 2004, (6): 61-67
[53] Kim S., Lee H., Kim J., et al. Genetic algorithms for the application of Activated Sludge Model No. 1[J]. Water Sci Technol, 2002, 45(4-5): 405-411
[54]高歌,罗金生.通用遗传算法估算化学及生化反应动力学参数[J].计算机与应用化学, 2005, 22(10): 915-917
[55]金一粟,周永华,梁逸曾.改进粒子群优化算法对反应动力学参数的估计[J].中南大学学报(自然科学版), 2008, 39(4): 694-699
[56]王晓哲,李界家,吴成东,等.多变量系统解耦方法综述[J].沈阳建筑工程学院学报, 2000, 16(2): 143-145
[57] Zhai L. F., Chai T. Y. Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models [J]. Journal of Control Theory and Applications, 2006, 4(1): 62-69
[58]袁晓红,王旭仁,柏玲.神经网络解耦控制在多变量控制系统中的应用[J].首都师范大学学报(自然科学版), 2008, 29(6): 20-22
[59]吴敏,周国雄,雷琪,等.多座不对称焦炉集气管压力模糊解耦控制[J].控制理论与应用, 2010, 27(1): 94-98
[60]李新利,白焰.一种基于互相关函数的神经网络在线解耦学习算法[J].系统仿真学报, 2005, 17(7): 1594-1600
[61]郑敏洁.基于Benchmark的污水生化处理过程控制策略研究[D].杭州:浙江工业大学, 2006
[62]胡慧,刘国荣.多变量系统模糊自适应解耦控制方法综述[J].湖南工程学院学报, 2004, 14(3): 11-13
[63]舒怀林. PID神经元网络对强耦合带时延多变量系统的解耦控制[J].控制理论与应用, 1998, 15(6): 920-924
[64] Kabouris J. C., Georgakakos A. P. Optimal control of the activated sludge process [J]. Water Research, 1990, 24(10):1197-1208
[65] Hamalainen R. P., Halme A., Gyllenberg A. A control model for activated sludge wastewater treatment process [A]. Proc. 6th IFAC World Congress[C]. Boston, 1980
[66] Marsili-Libelli S. Optimal control of the activated sludge process [J]. Tran. Inst. M. C, 1984, 6(3): 146-153
[67] Weijers S. R. Modelling, Identification and control of the activated sludge processes for Nitrogen removal [D]. Netherlands: Technical University of Eindhoven, 2000
[68] Lindberg C. F. Control and estimation strategies applied to the activated sludge process [D]. Uppsala:Uppsala University, 1997
[69] Lukasse L. J. S., Keesman K. J., van Straten G. A comparison of control strategies for alternating activated sludge processes [J]. Wat. Sci. Tech., 1999, 39(4): 93-102
[70] Kabouris J. C., Georgakakos A P. Stochastic control of the activated sludge process [J]. Wat. Sci. Tech., 1991, 24(6): 249-255
[71] Kabouris J. C., Georgakakos A. P., Camara A. Optimal control of the activated sludge process: Effect of sludge storage [J]. Wat. Res., 1992, 26(4): 507-517
[72] Ryckaert V. Model based optimization and control of bioprocesses, from theory to practice [D]. Belgium: Katholieke Universiteit Leuven, 1998
[73]彭永臻,王宝贞,王淑莹.活性污泥法的多变量最优控制I.基础理论与DO浓度对运行费用的影响[J].环境科学学报, 1998, 18(1): 11-19
[74]彭永臻,王宝贞,王淑莹.活性污泥法的多变量最优控制II.限制有机物(BOD)的排放总量[J].环境科学学报, 1998, 18(1): 20-27
[75]彭永臻,王宝贞,王淑莹.活性污泥法的多变量最优控制III.限制最高和同时限制平均与最高出水BOD浓度[J].环境科学学报, 1998, 18(2): 128-136
[76]刘载文,张春芝,王小艺,等.基于遗传算法的污水处理过程优化控制方法[J].计算机与应用化学, 2007, 24(7): 959-962
[77]杨晓宇,周佩玲,傅忠谦.人工免疫与网络安全[J].计算机仿真, 2001, 18(6): 83-85
[78]莫宏伟.人工免疫系统原理与应用[M].哈尔滨:哈尔滨工业大学出版社, 2003
[79]蔡自兴,龚涛.免疫算法研究的进展[J].控制与决策, 2004, l9(8): 841-846
[80]焦李成,杜海峰,刘芳,等.免疫优化计算、学习与识别[M].北京:科学出版社, 2006, 48-51
[81]焦李成,尚荣华,马文萍,等.多目标优化免疫算法、理论和应用[M].北京:科学出版社, 2010, 43-48
[82] Forrest S., Perelson A. S., Allen L.,et al. Self-nonself discrimination in a computer[A]. Proceedings of IEEE Symposium on Research in Security and Privacy[C]. Oakland, CA: IEEE Press, 1994:202-212
[83] D'haeseleer P. Forrest S. Helman P. An immunological approach to change detection: algorithms, analysis and implications[C]. Proceedings of IEEE Symposium on Research in Security and Privacy, Las Alamitos, CA, USA, 1996: 110-119
[84] Forrest S., Hofmeyr S. A. Immunology as information processing[C]. In: Segel L A, Cohen I R, eds. Design Principles for the Immune System and Other Distributed Autonomous Systems.USA: Oxford University Press, 2000
[85] Aydin I., Karakose M., Akin E. Chaotic-based hybrid negative selection algorithm and its applications in fault and anomaly detection [J]. Expert System with Applications, 2010, 37(7): 5285-5294
[86] Zhang P. T., Wang W., Tan Y. A malware detection model based on a negative selection algorithm with penalty factor [J]. SCIENCE CHINA Information Sciences (Science in China Series F), 2010, 53(12): 2461-2471
[87] Igawa K., Ohashi H. A negative selection algorithm for classification and reduction of the noise effect [J]. Applied Soft Computing, 2009, 9 (1):431-438
[88]罗一丹,蔡自兴,龚涛,等.一种新的免疫进化算法在函数优化中的应用[J].计算机工程与科学, 2008, 30(8): 49-52
[89] de Castro L. N., Von Zuben F. J. The clonal selection algorithm with engineering applications [A]. Proceedings of GECCO’00, Workshop on Artificial Immune Systems and Their Applications[C], 2000: 36-37
[90] Kim J., Bentley P. J. Towards an artificial immune system for network intrusion detection: An investigation of dynamic clonal selection [A]. Proceedings of Congress on Evolutionary Computiation[C], Hawaii, 2002:1015-1020
[91]李凤萍.几类人工免疫算法及其应用研究[D].长春:吉林大学, 2009
[92]钱海,马建辉,王煦法.一种新的基于克隆选择原理的人工免疫算法[J].中国科学技术大学学报, 2008, 38(12): 1397-1406
[93]张瑜,李涛,吴丽华,等.求解TSP问题的抗体克隆优化算法[J].四川大学学报(工程科学版), 2010, 42(3): 127-131
[94]刘琼,吴小俊.一种改进的免疫克隆选择算法[J].山东大学学报(工学版), 2009, 39(6): 8-12
[95]王炼红,章兢,龚固丰,等.改进的克隆选择算法求解0-1背包问题[J].湖南大学学报(自然科学版), 2009, 36(3): 81-84
[96] Gao S., Tang Z., Dai H., et al. A hybrid clonal selection algorithm [J]. International Journal of Innovative Computing, Information and Control, 2008, 4(4): 995-1008
[97]刘朝华,张英杰,吴建辉.一种求解TSP问题的改进克隆选择算法[J].系统仿真学报, 2010, 22(7): 1627-1631
[98]钟燕飞,张良培,李平湘.基于非一致性自适应变异的克隆选择算法[J].武汉大学学报(信息科学版), 2009, 34(3): 308-312.
[99] Lu H., Yang J. An improved clonal selection algorithm for job shop scheduling [A]. In: International symposium on intelligent ubiquitous computing and education [C]. Piscataway: IEEE Computer Society, 2009: 34-37
[100] Du H. F., Gong M. G., Liu R. C.,et al. Adaptive chaos clonal evolutionary programming algorithm [J]. Science in China Series F: Information Sciences, 2005, 48(5): 579-595
[101] Gao X.Z., Wang X., Ovaska S.J. Fusion of clonal selection algorithm and differential evolution method in training cascade-correlation neural network [J]. Neurocomputing, 2009,72 (10-12): 2483-2490
[102] Liu R. C., Jiao L. C., Du H. F. Clonal strategy algorithm based on the immune memory [J]. Journal of Computer Science and Technology, 2005, 20(5): 728-734
[103]杨咚咚,焦李成,公茂果,等.求解偏好多目标优化的克隆选择算法[J].软件学报, 2010, 21(1): 14-33
[104]洪露,纪志成,龚成龙.一种改进型克隆选择算法及其几乎处处强收敛性研究[J].控制与决策, 2010, 5(5): 725-729
[105]方贤进,李龙.一般克隆选择算法的收敛性证明[J].计算机应用研究, 2010, 27(5): 1683-1685
[106]叶文,欧阳中辉,朱爱红,等.求解多峰函数优化的小生境克隆选择算法[J].系统工程与电子技术, 2010, 32(5): 1100-1104
[107]魏圆圆,唐超礼,黄友锐.自适应克隆选择算法及其仿真研究[J].模式识别与人工智能, 2009, 22(2): 202-207
[108]徐雪松,章兢,周泉,等.一种基于免疫抗体聚类算法的复杂函数寻优[J].湖南大学学报(自然科学版), 2007, 34(9): 39-43
[109]钟燕飞,张良培,龚健雅,等.基于人工免疫系统的遥感图像分类[J].遥感学报, 2005, 9(4): 374- 380
[110]张灵,陈晓宏,翁毅,等.克隆选择算法在遥感影像分类中的应用[J].中山大学学报(自然科学版), 2008, 47(3): 128-132
[111]林可鸿,贺益君,陈德钊.有约束过程动态优化问题的改进克隆选择算法[J].高校化学工程学报, 2009, 23(5): 858-863
[112]董永贵,孙照焱,贾惠波.振动信号异常值的免疫机制检测算法[J].清华大学学报(自然科学版), 2004, 44 (5): 625-628
[113] Dasgupta D., Forrest S.Novelty detection in time series data using ideas from immunology[C]. Proceedings of the 5th International Conference on Intelligent Systems. Reno, 1996: 82-87
[114] Gonzalez F. A., Dasgupta D. Anomaly Detection Using Real-valued Negative Selection [J]. Genetic Programming and Evolvable Machines, 2003, 4(4): 383-403
[115] Laurentys C. A., Ronacher G., Palhares R. M. et al. Design of an Artificial Immune System for fault detection: A Negative Selection Approach [J]. Expert Systems with Applications, 2010, 37(7): 5507-5513
[116] Stibor T., Mohr P., Timmis J., et al. Is negative selection appropriate for anomaly detection? [A]. Proceedings of the genetic and evolutionary computation conference[C]. Washington: ACM Press, 2005: 321–328.
[117] Dasgupta D., Gonza'lez F. An immunity-based technique to characterize intrusions in computer networks [J]. IEEE Trans Evol Comput, 2002, 6(3): 281-291
[118] Gao X. Z., Ovaska S. J., Wang X. A neural networks-based negative selection algorithm in fault diagnosis [J]. Neural Computing and Applications, 2008, 17(1): 91-98
[119] Dasgupta D., KrishnaKumar K., Wong D., et al. Negative Selection Algorithm for Aircraft Fault Detection[C]. Proc. Third International Conference on Artificial Immune Systems, Catania, Italy, 2004: 1-14
[120]何申,罗文坚,王煦法.一种检测器长度可变的非选择算法[J].软件学报, 2007, 18(6): 1361-1368
[121]刘星宝,蔡自兴.异常检测系统的漏洞分析[J].中南大学学报(自然科学版), 2009, 40(4): 986-992
[122]王辉,王科俊,于立君,等.一种基于模糊思想的变阈值免疫阴性选择算法[J].哈尔滨工程大学学报, 2007, 28(11): 1222-1227
[123]汪慧敏.基于负选择算法故障检测的研究[D].哈尔滨:哈尔滨工业大学, 2007
[124] RI S.I.,侯德刚,张振家,等.基于BP人工神经网络的生化处理水水质预测[J].现代化工, 2009, 29(12): 66-70
[125] Vapnik V. N. The nature of statistical learning theory [M]. New York: Speringer-Verlag, 1995
[126] Vapnik V. N. The nature of statistical learning theory [M]. 2nd Edition. New York: Speringer-Verlag, 1999
[127] Vapnik V. N. An overview of statistical learning theory [J]. IEEE Transactions on Neural Networks, 1999, 10(5): 988 - 999
[128] Cortes C., Vapnik V. N. Support vector networks [J]. Machine Learning, 1995, 20(3): 273-297
[129]焦李成,周伟达,张莉,等.智能目标识别与分类[M].北京:科学出版社, 2010, 4-10
[130]许建华,张学工,李衍达.支持向量机的新发展[J].控制与决策, 2004, 19(5): 481-485
[131]唐浩,屈梁生.基于支持向量机的发动机故障诊断[J].西安交通大学学报, 2007, 41(9): 1124-1126
[132]范昕炜,杜树新,吴铁军.粗SVM分类方法及其在污水处理过程中的应用[J].控制与决策, 2004, 19(5): 573-576
[133]许玉格,罗飞,余仁辉.模糊粗糙集在污水处理过程中的应用[J].自动化与仪表, 2010, 25(2): 9-11
[134] Müller K. R., Smola A J, R?tsch G, et al. Predicting Time Series with Support Vector Machines[A], Proceedings of the 7th International Conference on Artificial Neural Networks[C]. London:Springer-Verlag,1997:999-1004
[135] Tay F. E. H., Cao L. J. Application of support vector machines in financial time series forecasting [J]. Omega, 2001, 29(4):309-317
[136]阎威武,常俊林,邵惠鹤.基于滚动时间窗的最小二乘支持向量机回归估计方法及仿真[J],上海交通大学学报, 2004, 38(4):524-526
[137]王凌云,杜卫华,刘梅花,等.基于改进在线支持向量回归的离子浓度预测模型[J],控制与决策, 2009, 24(4): 537-541
[138]林连雷,姜守达,刘晓东.基于微分进化算法的SVM参数选择[J].哈尔滨工程大学学报, 2009, 30(2): 138-141
[139] Wu Q., Law R. The forecasting model based on modified SVRM and PSO penalizing Gaussian noise [J]. Expert Systems with Applications, 2011, 38(3): 1887-1894
[140] Wang W., Li X.Y., Zhang R.B. Boundary processing of HHT using support vector regression machines [A]. Lecture Notes in Computer Science [C]. Heidelberg:Springer Verlag, 2007: 174-177
[141] Zhou J.Z., Huang J., Zhou J., et al. Friction modelling based on support vector regression machines and genetic algorithms [A]. IEEE/ASME International Conference on Advanced Intelligent Mechatronics [C]. Piscataway:Institute of Electrical and Electronics Engineers Inc., 2008: 1076-1081
[142] Yu Q., Liu Y., Rao F. Parameter selection of support vector regression machine based on differential evolution algorithm [A]. 6th International Conference on Fuzzy Systems and Knowledge Discovery [C]. Piscataway: IEEE Computer Society, 2009: 596-598
[143]王定成.支持向量机建模预测与控制[M].北京:气象出版社, 2009:12-37
[144] Cristianini N., Taylor J. S.著,李国正,王猛,曾华军,译.支持向量机导论[M].北京:电子工业出版社, 2004
[145] Müller K. R., Mika S., R?tsch G., et al. An Introduction to Kernel-Based Learning Algorithms [J]. IEEE Transactions on Neural Networks, 2001, 12(2): 181-201
[146] Papamichail I., Adjiman C. S. Global optimization of dynamic systems [J]. Computers and Chemical Engineering, 2004, 28(3): 403-415
[147] Moles C. G., Mendes P., Banga J. R. Parameter estimation in biochemical pathways: A comparison of global optimization methods [J]. Genome Research, 2003, 13(11): 2467-2474
[148]王建平,程声通,贾海峰.环境模型参数识别方法研究综述[J].水科学进展, 2006, 17(4): 574-580
[149]郭向红,孙西欢,马娟娟.基于混合遗传算法估计van Genuchten方程参数[J].水科学进展, 2009, 20(5): 677-682
[150] Chowdhury G. M., Roy G. G. Application of Genetic Algorithm (GA) to estimate the rate parameters for solid state reduction of iron ore in presence of graphite [J]. Computational Materials Science, 2009, 45(1): 176-180
[151] Gong M. G., Jiao L. C., Zhang X. R. A population-based artificial immune system for numerical optimization [J]. Neurocomputing, 2008, 72 (1-3): 149-161
[152] Lau H. Y. K., Tsang W. W. P. A parallel immune optimization algorithm for numeric function optimization [J], Evolutionary Intelligence, 2009, 1(3): 171-185
[153] Dai Y. S., Li Y. Y., Wei L., et al. Adaptive immune-genetic algorithm for global optimization to multivariable function [J], Journal of Systems Engineering and Electronics, 2007, 18(3): 655-660
[154] De Castro L. N., Von Zuben F. J. Learning and optimization using the clonal selection principle[J], IEEE Transactions on Evolutionary Computation, 2002, 6(3): 239-251
[155] Park H., Kwak N. S., Lee J. A method of multiobjective optimization using a genetic algorithm and an artificial immune system [J], Journal of Mechanical Engineering Science, 2009, 223(5): 1243-1252
[156] Woldemariam K. M., Yen G. G. Vaccine-enhanced artificial immune system for multimodal function optimization[J], IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010, 40(1): 218-228
[157] Aragón V. S., Esquivel S. C., Coello Coello C. A. Artificial immune system for solvingglobal optimization problems[J], Inteligencia Artificial, 2010, 46(14): 3-16
[158] Acan A. Clonal Selection Algorithm with Operator Multiplicity[C]// Proc. of the 2004 Congress on Evolutionary Computation, Portland, OR: IEEE Press, 2004: 1909-1915
[159]郭一楠,王辉,程健.自适应免疫克隆选择文化算法[J],电子学报, 2010, 38(4): 966-972
[160] Becerra R. L., Coello Coello C. A. Cultured differential evolution for constrained optimization[J], Computer Methods in Applied Mechanics and Engineering, 2006, 195(33-36): 4303-4322
[161]李中华,张雨浓,谭洪舟,等.一类具有精英学习能力的增强型人工免疫网络优化算法[J],控制理论与应用, 2009, 26(3): 283-290
[162]张海英,韩贵金,潘永湘.混沌免疫进化算法及其在函数优化中的应用[J].模式识别与人工智能, 2007, 20(2): 225-229
[163]卢培利,张代钧,刘颖,等.活性污泥法动力学模型研究进展和展望[J].重庆大学学报(自然科学版), 2002, 25(3): 109-114
[164]宋张天,张代钧,卢培利,等.基于微分代数方法的生物除磷模型参数结构可识别性研究[J].环境科学学报, 2010, 30(1): 97-102
[165]张著洪,钱淑渠.自适应免疫算法及其对动态函数优化的跟踪[J].模式识别与人工智能, 2007, 20(1): 85-94
[166]焦李成,公茂果,王爽,等.自然计算、机器学习与图像处理前沿[M].西安:西安电子科技大学出版社, 2008: 13-18
[167]薛明志,钟伟才,刘静,等.用于函数优化的正交Multi-Agent遗传算法[J].系统工程与电子技术, 2004, 26(9): 1305-1311
[168] Yao X., Liu Y., Lin G. M. Evolutionary Programming Made Faster[J], IEEE Transactions on Evolutionary Computation, 1999, 3 (2) : 82-102
[169]朱筱蓉,张兴华.基于小生境遗传算法的多峰函数全局优化研究[J],南京工业大学学报, 2006, 28(3): 39-43
[170]张晶,姚重华.活性污泥过程模型中的动力学参数[J].大连铁道学院学报, 1998, 19(2): 94-97
[171]李雯.基于ASM1与流态相结合的三沟式氧化沟系统数学模型的初步研究[D].邯郸:河北工程大学, 2009
[172]姚重华.废水处理计量学导论[M].北京:化学工业出版社, 2002: 1-15
[173]张庆岚,杨三青.油田污水处理最优控制系统研究[J].工业仪表与自动化装置, 2002, 4: 31-33
[174]舒怀林. PID神经元网络及其控制系统[M].北京:国防工业出版社, 2006: 1-9
[175]舒怀林,郭秀才.多变量强耦合时变系统的PID神经网络控制[J].工矿自动化, 2003, (5):16-18
[176]潘海鹏,徐玉颖.基于BP网络的流浆箱双变量PID解耦控制[J].化工学报, 2010, 61(8): 2154-2158
[177]朴海国,王志新,张华强.基于合作粒子群算法的PID神经网络非线性控制系统[J].控制理论与应用, 2009, 26(12): 1317-1324
[178]焦竹青,屈百达,徐保国.基于RBF神经网络的多变量系统PID解耦控制[J].系统仿真学报, 2008, 20(2): 627-630
[179]付龙海,李蒙.基于PID神经网络解耦控制的变风量空调系统[J].西南交通大学学报, 2005, 40(1): 13-17
[180]孙建华,汪伟,郑可为.船用核动力装置二回路PID神经网络解耦控制研究[J].哈尔滨工程大学学报, 2007, 28(6): 656-659
[181]郑安平.基于PID神经网络的三自由度飞行器模型控制研究[D].南京:南京理工大学, 2008
[182]王介生,丛峰武,张勇.基于粒子群优化算法的球磨机制粉系统PID-ANN解耦控制器[J].化工学报, 2008, 17(7): 1743-1748
[183] Wang J. J., An D.W., Zhang C.F., et al. Genetic Optimization Algorithm of PID Decoupling Control for VAV Air Conditioning System [J]. TRANSACTIONS OF TIANJIN UNIVERSITY, 2009, 15(4): 308-314
[184]薛文涛,吴晓蓓,翟玉强,等.三自由度飞行器模型的神经网络PID控制[J].控制工程, 2009, 16(2): 214-219
[185]傅强,樊丁.航空发动机PID神经网络解耦控制[J].推进技术, 2007, 28(2): 208-210
[186] Zhang, D.P., Zhai W.P., Wu A.G, et al. Decoupling control of forging machine based on PID neural network [A]. Proceedings of the 8th World Congress on Intelligent Control and Automation [C]. Piscataway: Institute of Electrical and Electronics Engineers Inc., 2010: 1858-1862
[187]程启明,程尹曼,汪明媚,等.球磨机混合优化前向神经网络PID解耦控制系统[J].电力系统及其自动化学报, 2010, 22(2): 54-59
[188]杨进,刘晓洁,李涛.人工免疫系统中变异算法研究[J].计算机工程, 2007, 33(17): 37-39
[189] Zhao X.C., Liu G.L., Liu H.Q., et al. A new clonal selection immune algorithm with perturbation guiding search and non-uniform hypermutation [J]. International Journal of Computational Intelligence Systems, 2010, 3(Suppl. 1): 1-17
[190]薛文涛,吴晓蓓,徐志良.基于双变异算子的免疫规划[J].控制与决策, 2007, 22(12): 1411-1416
[191]薛文涛,吴晓蓓,徐志良.一种基于双变异算子的免疫网络算法[J].控制与决策, 2008, 23(12): 1417-1422
[192]姜新农,王文香.基于改进免疫克隆算法的BP网络权值优化[J].计算机仿真, 2007, 24(2): 165-167
[193]杨铁军,吴效明.基于加权变异免疫算法的微钙化点特征选择[J].计算机应用研究, 2008, 25(11): 3302-3304
[194]何宏,钱锋.基于免疫网络理论的动态超变异免疫算法[J].控制与决策. 2008, 23(6): 613-618
[195] de Franca F. O., Von Zuben F. J., de Castro L. N. An artificial immune network for multimodal function optimization on dynamic environments [A]. GECCO’05 [C], Washington: GECCO, 2005: 289-296
[196] Cutello V., Nicosia G., Pavone M. Real coded clonal selection algorithm for unconstrained global optimization using a hybrid inversely proportional hypermutation operator [A]. SAC’06 [C], France: SAC, 2006: 950-954
[197]曾光明,秦肖生,何理,等.神经网络智能控制系统应用于废水处理[J].中国给水排水, 2002, 18 (2): 17-19
[198]蔡自兴,徐光.人工智能及其应用[M].第3版.北京:清华大学出版社, 2004: 190-196
[199]张著洪,黄席樾.一种新的免疫算法及其在多模态函数优化中的应用[J].控制理论与应用, 2004, 21(1): 17-21
[2]周生贤. 2009年中国环境状况公报[R].北京:中国环境保护部, 2010
[3] Zhang X. X., Zhao D. Y., Wang, Z. X., et al. Environmental biologicalmodel based on optimization of activated sludge process [J]. International Journal of Environmental Science and Technology, 2009, 6(1): 69-76
[4]徐亚同,黄民生.废水生物处理的运行管理与异常对策[M].北京:化学工业出版社, 2003: 91-92
[5]易赛莉,卢磊.城市污水处理可持续发展工艺选型和技改方法初探[J].环境科学与技术, 2007, 30(8): 60-63
[6]王华丰.基于ASM2的A2/O工艺机理模型及自动控制研究[D].杭州:浙江大学,2005
[7]高大文,彭永臻.实时控制技术在污水生物处理中的研究进展[J].应用与环境生物学报, 2004, 10(6): 821-825
[8] Albazzaz H., Wang X. Z., Marhoon F. Multidimensional visualisation for process historical data analysis-a comparative study with multivariate statistical process control [J]. Journal of Process Control, 2005, 15(3): 285-294
[9] Albazzaz H., Wang X. Z. Historical data analysis based on plots of independent and parallel coordinates and statistical control limits [J]. Journal of Process Control, 2006, 16(2): 103-114
[10] Bao H., Khan F., Iqbal T., et al. Risk-based fault diagnosis and safety management for process systems [J]. Process Safety Progress, 2011, 30(1): 6-17
[11] Hong Y. S. T., Rosen M. R., Bhamidimarri R. Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis [J]. Water Research, 2003, 37(7): 1608-1618
[12]李晓东,曾光明,蒋茹,等.改进支持向量机对污水处理厂运行状况的故障诊断[J].湖南大学学报(自然科学版), 2007, 34(12): 68-71
[13] Zeng G. M., Li X. D., Jiang R. et al. Fault Diagnosis of WWTP Based on ImprovedSupport Vector Machine [J]. Environmental Engineering Science, 2006, 23(6): 1044-1054
[14]杨红,高月芳,罗飞.基于人工免疫和ICA的污水进水水质LS-SVM分类[J].自动化与仪表, 2010, (6): 1-4
[15] Dellana S.A., West D. Predictive modeling for wastewater applications: Linear and nonlinear approaches [J]. Environmental Modelling and Software, 2009, 24(1): 96-106
[16] Akratos C.S., Papaspyros, J.N.E., Tsihrintzis V. A., et al. Artificial neural network use in ortho-phosphate and total phosphorus removal prediction in horizontal subsurface flow constructed wetlands [J]. Biosystems Engineering, 2009, 102(2):190-201
[17] Hamed M. M., Khalafallah M. G., Hassanien E. A. Prediction of wastewater treatment plant performace using artificial neural networks [J]. Environmental Modelling & Software, 2004, 19(10): 919-928
[18] Mjalli F. S., Al-Asheh S., Alfadala H. E. Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance [J]. Journal of Environmental Management, 2007, 83(3): 329-338
[19] Raduly B., Gernaey K. V., Capodaglio A. G., et al. Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study [J]. Environmental Modelling & Software, 2007, 22(8), 1208-1216
[20] Molga E., Cherbanski R., Szpyrkowicz L. Modeling of an industrial full-scale plant for biological treatment of textile wastewater: Application of neural network [J]. Industrial & Engineering Chemistry Research, 2006, 45(3) : 1039-1046
[21] Machon I., Lopez H., Rodriguez-Iglesias J., et al. Simulation of a coke wastewater nitrification process using a feed-forward neuronal net [J]. Environmental Modelling & Software, 2007, 22(9): 1382-1387
[22]韦安磊,曾光明,黄国和,等.生物脱氮除磷活性污泥系统复合模拟方法[J].环境工程学报, 2010, 4(11): 2590-2594
[23] RI S.I.,侯德刚,张振家,等.基于BP人工神经网络的生化处理水水质预测[J].现代化工, 2009, 29(12): 66-70
[24]楼文高,刘遂庆.活性污泥系统神经网络建模与仿真研究[J].环境污染与防治, 2005, 27(9): 704-707
[25]乔俊飞,韩红桂. RBF神经网络的结构动态优化设计[J].自动化学报, 2010, 36(6):865-872
[26] Hanbay D., Turkoglu I., Demir Y. Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks [J]. Expert Systems with Applications, 2008, 34(2): 1038-1043
[27] Pai T. Y. , Wan T. J., Hsu S. T., et al. Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent [J]. Computers and Chemical Engineering, 2009, 33(7): 1272-1278
[28]李明河,杨明.污水总氮软测量模型的研究与应用[J].自动化与仪表, 2009, 24(9): 1-4
[29] Luo F., Yu R.H., Xu Y.G., et al. Effluent quality prediction of wastewater treatment plant based on fuzzy-rough sets and artificial neural networks [A]. The 6th International Conference on Fuzzy Systems and Knowledge Discovery[C]. Piscataway: IEEE Computer Society, 2009: 47-51
[30]郑广勇,罗飞,陈伟斌.基于免疫粒子群神经网络的污水水质预测[J].微处理机, 2010, 31(2): 75-81
[31]韩耀宗,黄亮亮,宋新山.基于小波神经网络的芦苇潜流人工湿地水质预测[J].环境科学研究, 2009, 22(12): 1460-1465
[32]朱启兵,黄敏,崔宝同.基于小波神经网络的污水处理厂出水水质预测[J].计算机工程与应用, 2007, 43(28): 15-17
[33]丛秋梅,柴天佑,余文.带有稳定学习算法的小波神经网络及应用[J].东北大学学报(自然科学版), 2010, 31(3): 305-308
[34] Hong Y. S., Rosen M.R., Bhamidimarri R. Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis [J]. Water Research, 2003, 37(7): 1608-1618
[35] Cinar O. New tool for evaluation of performance of wastewater treatment plant: Artificial neural network [J]. Process Biochemistry. 2005, 40(9): 2980-2984
[36] Grieu S., Thiery F., Traore A., et al. KSOM and MLP neural networks for on-line estimating the efficiency of an activated sludge process [J]. Chemical Engineering Journal, 2006, 116(1): 1-11
[37] Fernandez F. J., Seco A., Ferrer J., et al. Use of neurofuzzy networks to improvewastewater flow-rate forecasting [J]. Environmental Modelling & Software, 2009, 24(6): 686-693
[38]王海云,冯裕钊,张晓清,等.一种基于补偿模糊神经网络的水质预测方法[J].重庆建筑大学学报, 2004, 26(5): 77-81
[39]冯裕钊,卓明,田利强,等.聚类方法在污水处理软测量中的应用机理研究[J].后勤工程学院学报, 2007, 23(1): 60-65
[40]余伟,罗飞,杨红,等.基于多神经网络的污水氨氮预测模型[J].华南理工大学学报(自然科学版), 2010, 38(12): 79-83
[41] Ye H.T., Luo F., Xu Y.G. Application of RBF Network Based on Immune Algorithm to Predicting of Wastewater Treatment [J]. Lecture Notes in Computer Science, 2009, 5553(3): 1197-1202
[42] Clara N. Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants [J]. WSEAS Transactions on Systems, 2008, 7(6): 695-705
[43]李峰,吴敏,杨哲.活性污泥数学模型在我国的研究进展[J].中国资源综合利用, 2007, 25(11): 21-23
[44] Henze M., Grady C. P., Gujor W., et al. Activated sludge model NO.1 [R]. London , England: Int. Association on Water Pollution Research and Control (IAWPRC), Scientific and Technical Report N0.1, 1986
[45] Henze M., Gujor W., Mino T., et al. Activated sludge model N0.2 [R]. London ,England: Int. Association on Water Pollution Research and Control (IAWPRC), Scientific and Technical Report N0.2, 1995
[46] Gujor W., Henze M., Mino T., et al. Activated sludge model N0.3 [J]. Water Science and Technology. 1999, 39(1): 183-193
[47]刘光莲.活性污泥数学模型在污水处理中的研究和应用进展[J].水科学与工程技术, 2009, (1): 31-33
[48]朱嵩,毛根海,程伟平,等.基于贝叶斯推理的水环境系统参数识别[J].江苏大学学报(自然科学版), 2007, 28(3): 237-240
[49]邓义祥.稀疏数据条件下河流水质模型的参数识别[D].北京:清华大学, 2003
[50] Rojas R., Feyen L., Dassargues A. Conceptual model uncertainty in groundwater modeling: Combining generalized likelihood uncertainty estimation and Bayesian model averaging [J]. Water Resources Research, 2008, 44(12): 1-16
[51]王建平,程声通,贾海峰.环境模型参数识别方法研究综述[J].水科学进展, 2006, 17(4): 574-580
[52]王薇,曾光明,何理.用模拟退火算法估计水质模型参数[J].水利学报, 2004, (6): 61-67
[53] Kim S., Lee H., Kim J., et al. Genetic algorithms for the application of Activated Sludge Model No. 1[J]. Water Sci Technol, 2002, 45(4-5): 405-411
[54]高歌,罗金生.通用遗传算法估算化学及生化反应动力学参数[J].计算机与应用化学, 2005, 22(10): 915-917
[55]金一粟,周永华,梁逸曾.改进粒子群优化算法对反应动力学参数的估计[J].中南大学学报(自然科学版), 2008, 39(4): 694-699
[56]王晓哲,李界家,吴成东,等.多变量系统解耦方法综述[J].沈阳建筑工程学院学报, 2000, 16(2): 143-145
[57] Zhai L. F., Chai T. Y. Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models [J]. Journal of Control Theory and Applications, 2006, 4(1): 62-69
[58]袁晓红,王旭仁,柏玲.神经网络解耦控制在多变量控制系统中的应用[J].首都师范大学学报(自然科学版), 2008, 29(6): 20-22
[59]吴敏,周国雄,雷琪,等.多座不对称焦炉集气管压力模糊解耦控制[J].控制理论与应用, 2010, 27(1): 94-98
[60]李新利,白焰.一种基于互相关函数的神经网络在线解耦学习算法[J].系统仿真学报, 2005, 17(7): 1594-1600
[61]郑敏洁.基于Benchmark的污水生化处理过程控制策略研究[D].杭州:浙江工业大学, 2006
[62]胡慧,刘国荣.多变量系统模糊自适应解耦控制方法综述[J].湖南工程学院学报, 2004, 14(3): 11-13
[63]舒怀林. PID神经元网络对强耦合带时延多变量系统的解耦控制[J].控制理论与应用, 1998, 15(6): 920-924
[64] Kabouris J. C., Georgakakos A. P. Optimal control of the activated sludge process [J]. Water Research, 1990, 24(10):1197-1208
[65] Hamalainen R. P., Halme A., Gyllenberg A. A control model for activated sludge wastewater treatment process [A]. Proc. 6th IFAC World Congress[C]. Boston, 1980
[66] Marsili-Libelli S. Optimal control of the activated sludge process [J]. Tran. Inst. M. C, 1984, 6(3): 146-153
[67] Weijers S. R. Modelling, Identification and control of the activated sludge processes for Nitrogen removal [D]. Netherlands: Technical University of Eindhoven, 2000
[68] Lindberg C. F. Control and estimation strategies applied to the activated sludge process [D]. Uppsala:Uppsala University, 1997
[69] Lukasse L. J. S., Keesman K. J., van Straten G. A comparison of control strategies for alternating activated sludge processes [J]. Wat. Sci. Tech., 1999, 39(4): 93-102
[70] Kabouris J. C., Georgakakos A P. Stochastic control of the activated sludge process [J]. Wat. Sci. Tech., 1991, 24(6): 249-255
[71] Kabouris J. C., Georgakakos A. P., Camara A. Optimal control of the activated sludge process: Effect of sludge storage [J]. Wat. Res., 1992, 26(4): 507-517
[72] Ryckaert V. Model based optimization and control of bioprocesses, from theory to practice [D]. Belgium: Katholieke Universiteit Leuven, 1998
[73]彭永臻,王宝贞,王淑莹.活性污泥法的多变量最优控制I.基础理论与DO浓度对运行费用的影响[J].环境科学学报, 1998, 18(1): 11-19
[74]彭永臻,王宝贞,王淑莹.活性污泥法的多变量最优控制II.限制有机物(BOD)的排放总量[J].环境科学学报, 1998, 18(1): 20-27
[75]彭永臻,王宝贞,王淑莹.活性污泥法的多变量最优控制III.限制最高和同时限制平均与最高出水BOD浓度[J].环境科学学报, 1998, 18(2): 128-136
[76]刘载文,张春芝,王小艺,等.基于遗传算法的污水处理过程优化控制方法[J].计算机与应用化学, 2007, 24(7): 959-962
[77]杨晓宇,周佩玲,傅忠谦.人工免疫与网络安全[J].计算机仿真, 2001, 18(6): 83-85
[78]莫宏伟.人工免疫系统原理与应用[M].哈尔滨:哈尔滨工业大学出版社, 2003
[79]蔡自兴,龚涛.免疫算法研究的进展[J].控制与决策, 2004, l9(8): 841-846
[80]焦李成,杜海峰,刘芳,等.免疫优化计算、学习与识别[M].北京:科学出版社, 2006, 48-51
[81]焦李成,尚荣华,马文萍,等.多目标优化免疫算法、理论和应用[M].北京:科学出版社, 2010, 43-48
[82] Forrest S., Perelson A. S., Allen L.,et al. Self-nonself discrimination in a computer[A]. Proceedings of IEEE Symposium on Research in Security and Privacy[C]. Oakland, CA: IEEE Press, 1994:202-212
[83] D'haeseleer P. Forrest S. Helman P. An immunological approach to change detection: algorithms, analysis and implications[C]. Proceedings of IEEE Symposium on Research in Security and Privacy, Las Alamitos, CA, USA, 1996: 110-119
[84] Forrest S., Hofmeyr S. A. Immunology as information processing[C]. In: Segel L A, Cohen I R, eds. Design Principles for the Immune System and Other Distributed Autonomous Systems.USA: Oxford University Press, 2000
[85] Aydin I., Karakose M., Akin E. Chaotic-based hybrid negative selection algorithm and its applications in fault and anomaly detection [J]. Expert System with Applications, 2010, 37(7): 5285-5294
[86] Zhang P. T., Wang W., Tan Y. A malware detection model based on a negative selection algorithm with penalty factor [J]. SCIENCE CHINA Information Sciences (Science in China Series F), 2010, 53(12): 2461-2471
[87] Igawa K., Ohashi H. A negative selection algorithm for classification and reduction of the noise effect [J]. Applied Soft Computing, 2009, 9 (1):431-438
[88]罗一丹,蔡自兴,龚涛,等.一种新的免疫进化算法在函数优化中的应用[J].计算机工程与科学, 2008, 30(8): 49-52
[89] de Castro L. N., Von Zuben F. J. The clonal selection algorithm with engineering applications [A]. Proceedings of GECCO’00, Workshop on Artificial Immune Systems and Their Applications[C], 2000: 36-37
[90] Kim J., Bentley P. J. Towards an artificial immune system for network intrusion detection: An investigation of dynamic clonal selection [A]. Proceedings of Congress on Evolutionary Computiation[C], Hawaii, 2002:1015-1020
[91]李凤萍.几类人工免疫算法及其应用研究[D].长春:吉林大学, 2009
[92]钱海,马建辉,王煦法.一种新的基于克隆选择原理的人工免疫算法[J].中国科学技术大学学报, 2008, 38(12): 1397-1406
[93]张瑜,李涛,吴丽华,等.求解TSP问题的抗体克隆优化算法[J].四川大学学报(工程科学版), 2010, 42(3): 127-131
[94]刘琼,吴小俊.一种改进的免疫克隆选择算法[J].山东大学学报(工学版), 2009, 39(6): 8-12
[95]王炼红,章兢,龚固丰,等.改进的克隆选择算法求解0-1背包问题[J].湖南大学学报(自然科学版), 2009, 36(3): 81-84
[96] Gao S., Tang Z., Dai H., et al. A hybrid clonal selection algorithm [J]. International Journal of Innovative Computing, Information and Control, 2008, 4(4): 995-1008
[97]刘朝华,张英杰,吴建辉.一种求解TSP问题的改进克隆选择算法[J].系统仿真学报, 2010, 22(7): 1627-1631
[98]钟燕飞,张良培,李平湘.基于非一致性自适应变异的克隆选择算法[J].武汉大学学报(信息科学版), 2009, 34(3): 308-312.
[99] Lu H., Yang J. An improved clonal selection algorithm for job shop scheduling [A]. In: International symposium on intelligent ubiquitous computing and education [C]. Piscataway: IEEE Computer Society, 2009: 34-37
[100] Du H. F., Gong M. G., Liu R. C.,et al. Adaptive chaos clonal evolutionary programming algorithm [J]. Science in China Series F: Information Sciences, 2005, 48(5): 579-595
[101] Gao X.Z., Wang X., Ovaska S.J. Fusion of clonal selection algorithm and differential evolution method in training cascade-correlation neural network [J]. Neurocomputing, 2009,72 (10-12): 2483-2490
[102] Liu R. C., Jiao L. C., Du H. F. Clonal strategy algorithm based on the immune memory [J]. Journal of Computer Science and Technology, 2005, 20(5): 728-734
[103]杨咚咚,焦李成,公茂果,等.求解偏好多目标优化的克隆选择算法[J].软件学报, 2010, 21(1): 14-33
[104]洪露,纪志成,龚成龙.一种改进型克隆选择算法及其几乎处处强收敛性研究[J].控制与决策, 2010, 5(5): 725-729
[105]方贤进,李龙.一般克隆选择算法的收敛性证明[J].计算机应用研究, 2010, 27(5): 1683-1685
[106]叶文,欧阳中辉,朱爱红,等.求解多峰函数优化的小生境克隆选择算法[J].系统工程与电子技术, 2010, 32(5): 1100-1104
[107]魏圆圆,唐超礼,黄友锐.自适应克隆选择算法及其仿真研究[J].模式识别与人工智能, 2009, 22(2): 202-207
[108]徐雪松,章兢,周泉,等.一种基于免疫抗体聚类算法的复杂函数寻优[J].湖南大学学报(自然科学版), 2007, 34(9): 39-43
[109]钟燕飞,张良培,龚健雅,等.基于人工免疫系统的遥感图像分类[J].遥感学报, 2005, 9(4): 374- 380
[110]张灵,陈晓宏,翁毅,等.克隆选择算法在遥感影像分类中的应用[J].中山大学学报(自然科学版), 2008, 47(3): 128-132
[111]林可鸿,贺益君,陈德钊.有约束过程动态优化问题的改进克隆选择算法[J].高校化学工程学报, 2009, 23(5): 858-863
[112]董永贵,孙照焱,贾惠波.振动信号异常值的免疫机制检测算法[J].清华大学学报(自然科学版), 2004, 44 (5): 625-628
[113] Dasgupta D., Forrest S.Novelty detection in time series data using ideas from immunology[C]. Proceedings of the 5th International Conference on Intelligent Systems. Reno, 1996: 82-87
[114] Gonzalez F. A., Dasgupta D. Anomaly Detection Using Real-valued Negative Selection [J]. Genetic Programming and Evolvable Machines, 2003, 4(4): 383-403
[115] Laurentys C. A., Ronacher G., Palhares R. M. et al. Design of an Artificial Immune System for fault detection: A Negative Selection Approach [J]. Expert Systems with Applications, 2010, 37(7): 5507-5513
[116] Stibor T., Mohr P., Timmis J., et al. Is negative selection appropriate for anomaly detection? [A]. Proceedings of the genetic and evolutionary computation conference[C]. Washington: ACM Press, 2005: 321–328.
[117] Dasgupta D., Gonza'lez F. An immunity-based technique to characterize intrusions in computer networks [J]. IEEE Trans Evol Comput, 2002, 6(3): 281-291
[118] Gao X. Z., Ovaska S. J., Wang X. A neural networks-based negative selection algorithm in fault diagnosis [J]. Neural Computing and Applications, 2008, 17(1): 91-98
[119] Dasgupta D., KrishnaKumar K., Wong D., et al. Negative Selection Algorithm for Aircraft Fault Detection[C]. Proc. Third International Conference on Artificial Immune Systems, Catania, Italy, 2004: 1-14
[120]何申,罗文坚,王煦法.一种检测器长度可变的非选择算法[J].软件学报, 2007, 18(6): 1361-1368
[121]刘星宝,蔡自兴.异常检测系统的漏洞分析[J].中南大学学报(自然科学版), 2009, 40(4): 986-992
[122]王辉,王科俊,于立君,等.一种基于模糊思想的变阈值免疫阴性选择算法[J].哈尔滨工程大学学报, 2007, 28(11): 1222-1227
[123]汪慧敏.基于负选择算法故障检测的研究[D].哈尔滨:哈尔滨工业大学, 2007
[124] RI S.I.,侯德刚,张振家,等.基于BP人工神经网络的生化处理水水质预测[J].现代化工, 2009, 29(12): 66-70
[125] Vapnik V. N. The nature of statistical learning theory [M]. New York: Speringer-Verlag, 1995
[126] Vapnik V. N. The nature of statistical learning theory [M]. 2nd Edition. New York: Speringer-Verlag, 1999
[127] Vapnik V. N. An overview of statistical learning theory [J]. IEEE Transactions on Neural Networks, 1999, 10(5): 988 - 999
[128] Cortes C., Vapnik V. N. Support vector networks [J]. Machine Learning, 1995, 20(3): 273-297
[129]焦李成,周伟达,张莉,等.智能目标识别与分类[M].北京:科学出版社, 2010, 4-10
[130]许建华,张学工,李衍达.支持向量机的新发展[J].控制与决策, 2004, 19(5): 481-485
[131]唐浩,屈梁生.基于支持向量机的发动机故障诊断[J].西安交通大学学报, 2007, 41(9): 1124-1126
[132]范昕炜,杜树新,吴铁军.粗SVM分类方法及其在污水处理过程中的应用[J].控制与决策, 2004, 19(5): 573-576
[133]许玉格,罗飞,余仁辉.模糊粗糙集在污水处理过程中的应用[J].自动化与仪表, 2010, 25(2): 9-11
[134] Müller K. R., Smola A J, R?tsch G, et al. Predicting Time Series with Support Vector Machines[A], Proceedings of the 7th International Conference on Artificial Neural Networks[C]. London:Springer-Verlag,1997:999-1004
[135] Tay F. E. H., Cao L. J. Application of support vector machines in financial time series forecasting [J]. Omega, 2001, 29(4):309-317
[136]阎威武,常俊林,邵惠鹤.基于滚动时间窗的最小二乘支持向量机回归估计方法及仿真[J],上海交通大学学报, 2004, 38(4):524-526
[137]王凌云,杜卫华,刘梅花,等.基于改进在线支持向量回归的离子浓度预测模型[J],控制与决策, 2009, 24(4): 537-541
[138]林连雷,姜守达,刘晓东.基于微分进化算法的SVM参数选择[J].哈尔滨工程大学学报, 2009, 30(2): 138-141
[139] Wu Q., Law R. The forecasting model based on modified SVRM and PSO penalizing Gaussian noise [J]. Expert Systems with Applications, 2011, 38(3): 1887-1894
[140] Wang W., Li X.Y., Zhang R.B. Boundary processing of HHT using support vector regression machines [A]. Lecture Notes in Computer Science [C]. Heidelberg:Springer Verlag, 2007: 174-177
[141] Zhou J.Z., Huang J., Zhou J., et al. Friction modelling based on support vector regression machines and genetic algorithms [A]. IEEE/ASME International Conference on Advanced Intelligent Mechatronics [C]. Piscataway:Institute of Electrical and Electronics Engineers Inc., 2008: 1076-1081
[142] Yu Q., Liu Y., Rao F. Parameter selection of support vector regression machine based on differential evolution algorithm [A]. 6th International Conference on Fuzzy Systems and Knowledge Discovery [C]. Piscataway: IEEE Computer Society, 2009: 596-598
[143]王定成.支持向量机建模预测与控制[M].北京:气象出版社, 2009:12-37
[144] Cristianini N., Taylor J. S.著,李国正,王猛,曾华军,译.支持向量机导论[M].北京:电子工业出版社, 2004
[145] Müller K. R., Mika S., R?tsch G., et al. An Introduction to Kernel-Based Learning Algorithms [J]. IEEE Transactions on Neural Networks, 2001, 12(2): 181-201
[146] Papamichail I., Adjiman C. S. Global optimization of dynamic systems [J]. Computers and Chemical Engineering, 2004, 28(3): 403-415
[147] Moles C. G., Mendes P., Banga J. R. Parameter estimation in biochemical pathways: A comparison of global optimization methods [J]. Genome Research, 2003, 13(11): 2467-2474
[148]王建平,程声通,贾海峰.环境模型参数识别方法研究综述[J].水科学进展, 2006, 17(4): 574-580
[149]郭向红,孙西欢,马娟娟.基于混合遗传算法估计van Genuchten方程参数[J].水科学进展, 2009, 20(5): 677-682
[150] Chowdhury G. M., Roy G. G. Application of Genetic Algorithm (GA) to estimate the rate parameters for solid state reduction of iron ore in presence of graphite [J]. Computational Materials Science, 2009, 45(1): 176-180
[151] Gong M. G., Jiao L. C., Zhang X. R. A population-based artificial immune system for numerical optimization [J]. Neurocomputing, 2008, 72 (1-3): 149-161
[152] Lau H. Y. K., Tsang W. W. P. A parallel immune optimization algorithm for numeric function optimization [J], Evolutionary Intelligence, 2009, 1(3): 171-185
[153] Dai Y. S., Li Y. Y., Wei L., et al. Adaptive immune-genetic algorithm for global optimization to multivariable function [J], Journal of Systems Engineering and Electronics, 2007, 18(3): 655-660
[154] De Castro L. N., Von Zuben F. J. Learning and optimization using the clonal selection principle[J], IEEE Transactions on Evolutionary Computation, 2002, 6(3): 239-251
[155] Park H., Kwak N. S., Lee J. A method of multiobjective optimization using a genetic algorithm and an artificial immune system [J], Journal of Mechanical Engineering Science, 2009, 223(5): 1243-1252
[156] Woldemariam K. M., Yen G. G. Vaccine-enhanced artificial immune system for multimodal function optimization[J], IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010, 40(1): 218-228
[157] Aragón V. S., Esquivel S. C., Coello Coello C. A. Artificial immune system for solvingglobal optimization problems[J], Inteligencia Artificial, 2010, 46(14): 3-16
[158] Acan A. Clonal Selection Algorithm with Operator Multiplicity[C]// Proc. of the 2004 Congress on Evolutionary Computation, Portland, OR: IEEE Press, 2004: 1909-1915
[159]郭一楠,王辉,程健.自适应免疫克隆选择文化算法[J],电子学报, 2010, 38(4): 966-972
[160] Becerra R. L., Coello Coello C. A. Cultured differential evolution for constrained optimization[J], Computer Methods in Applied Mechanics and Engineering, 2006, 195(33-36): 4303-4322
[161]李中华,张雨浓,谭洪舟,等.一类具有精英学习能力的增强型人工免疫网络优化算法[J],控制理论与应用, 2009, 26(3): 283-290
[162]张海英,韩贵金,潘永湘.混沌免疫进化算法及其在函数优化中的应用[J].模式识别与人工智能, 2007, 20(2): 225-229
[163]卢培利,张代钧,刘颖,等.活性污泥法动力学模型研究进展和展望[J].重庆大学学报(自然科学版), 2002, 25(3): 109-114
[164]宋张天,张代钧,卢培利,等.基于微分代数方法的生物除磷模型参数结构可识别性研究[J].环境科学学报, 2010, 30(1): 97-102
[165]张著洪,钱淑渠.自适应免疫算法及其对动态函数优化的跟踪[J].模式识别与人工智能, 2007, 20(1): 85-94
[166]焦李成,公茂果,王爽,等.自然计算、机器学习与图像处理前沿[M].西安:西安电子科技大学出版社, 2008: 13-18
[167]薛明志,钟伟才,刘静,等.用于函数优化的正交Multi-Agent遗传算法[J].系统工程与电子技术, 2004, 26(9): 1305-1311
[168] Yao X., Liu Y., Lin G. M. Evolutionary Programming Made Faster[J], IEEE Transactions on Evolutionary Computation, 1999, 3 (2) : 82-102
[169]朱筱蓉,张兴华.基于小生境遗传算法的多峰函数全局优化研究[J],南京工业大学学报, 2006, 28(3): 39-43
[170]张晶,姚重华.活性污泥过程模型中的动力学参数[J].大连铁道学院学报, 1998, 19(2): 94-97
[171]李雯.基于ASM1与流态相结合的三沟式氧化沟系统数学模型的初步研究[D].邯郸:河北工程大学, 2009
[172]姚重华.废水处理计量学导论[M].北京:化学工业出版社, 2002: 1-15
[173]张庆岚,杨三青.油田污水处理最优控制系统研究[J].工业仪表与自动化装置, 2002, 4: 31-33
[174]舒怀林. PID神经元网络及其控制系统[M].北京:国防工业出版社, 2006: 1-9
[175]舒怀林,郭秀才.多变量强耦合时变系统的PID神经网络控制[J].工矿自动化, 2003, (5):16-18
[176]潘海鹏,徐玉颖.基于BP网络的流浆箱双变量PID解耦控制[J].化工学报, 2010, 61(8): 2154-2158
[177]朴海国,王志新,张华强.基于合作粒子群算法的PID神经网络非线性控制系统[J].控制理论与应用, 2009, 26(12): 1317-1324
[178]焦竹青,屈百达,徐保国.基于RBF神经网络的多变量系统PID解耦控制[J].系统仿真学报, 2008, 20(2): 627-630
[179]付龙海,李蒙.基于PID神经网络解耦控制的变风量空调系统[J].西南交通大学学报, 2005, 40(1): 13-17
[180]孙建华,汪伟,郑可为.船用核动力装置二回路PID神经网络解耦控制研究[J].哈尔滨工程大学学报, 2007, 28(6): 656-659
[181]郑安平.基于PID神经网络的三自由度飞行器模型控制研究[D].南京:南京理工大学, 2008
[182]王介生,丛峰武,张勇.基于粒子群优化算法的球磨机制粉系统PID-ANN解耦控制器[J].化工学报, 2008, 17(7): 1743-1748
[183] Wang J. J., An D.W., Zhang C.F., et al. Genetic Optimization Algorithm of PID Decoupling Control for VAV Air Conditioning System [J]. TRANSACTIONS OF TIANJIN UNIVERSITY, 2009, 15(4): 308-314
[184]薛文涛,吴晓蓓,翟玉强,等.三自由度飞行器模型的神经网络PID控制[J].控制工程, 2009, 16(2): 214-219
[185]傅强,樊丁.航空发动机PID神经网络解耦控制[J].推进技术, 2007, 28(2): 208-210
[186] Zhang, D.P., Zhai W.P., Wu A.G, et al. Decoupling control of forging machine based on PID neural network [A]. Proceedings of the 8th World Congress on Intelligent Control and Automation [C]. Piscataway: Institute of Electrical and Electronics Engineers Inc., 2010: 1858-1862
[187]程启明,程尹曼,汪明媚,等.球磨机混合优化前向神经网络PID解耦控制系统[J].电力系统及其自动化学报, 2010, 22(2): 54-59
[188]杨进,刘晓洁,李涛.人工免疫系统中变异算法研究[J].计算机工程, 2007, 33(17): 37-39
[189] Zhao X.C., Liu G.L., Liu H.Q., et al. A new clonal selection immune algorithm with perturbation guiding search and non-uniform hypermutation [J]. International Journal of Computational Intelligence Systems, 2010, 3(Suppl. 1): 1-17
[190]薛文涛,吴晓蓓,徐志良.基于双变异算子的免疫规划[J].控制与决策, 2007, 22(12): 1411-1416
[191]薛文涛,吴晓蓓,徐志良.一种基于双变异算子的免疫网络算法[J].控制与决策, 2008, 23(12): 1417-1422
[192]姜新农,王文香.基于改进免疫克隆算法的BP网络权值优化[J].计算机仿真, 2007, 24(2): 165-167
[193]杨铁军,吴效明.基于加权变异免疫算法的微钙化点特征选择[J].计算机应用研究, 2008, 25(11): 3302-3304
[194]何宏,钱锋.基于免疫网络理论的动态超变异免疫算法[J].控制与决策. 2008, 23(6): 613-618
[195] de Franca F. O., Von Zuben F. J., de Castro L. N. An artificial immune network for multimodal function optimization on dynamic environments [A]. GECCO’05 [C], Washington: GECCO, 2005: 289-296
[196] Cutello V., Nicosia G., Pavone M. Real coded clonal selection algorithm for unconstrained global optimization using a hybrid inversely proportional hypermutation operator [A]. SAC’06 [C], France: SAC, 2006: 950-954
[197]曾光明,秦肖生,何理,等.神经网络智能控制系统应用于废水处理[J].中国给水排水, 2002, 18 (2): 17-19
[198]蔡自兴,徐光.人工智能及其应用[M].第3版.北京:清华大学出版社, 2004: 190-196
[199]张著洪,黄席樾.一种新的免疫算法及其在多模态函数优化中的应用[J].控制理论与应用, 2004, 21(1): 17-21