基于极值动力学的MEMETIC算法及其在非线性预测控制中的应用研究
|
摘要
以预测控制为典型代表的先进控制技术在过去的几十年中取得了许多重要成果和广泛应用。随着现代工业的发展,对大型、复杂和不确定性系统的控制品质要求不断提高,逐渐涉及到很多具有强非线性、难以用数学模型精确描述的复杂系统建模与实时优化问题,基于线性模型的预测控制的局限性日益明显。非线性预测控制的提出为解决上述问题提供了可行的途径,然而非线性模型的引入也带来了一系列理论与实际难题,如预测模型结构/参数选择、控制变量的滚动优化等。这就促使工业过程领域的学者们致力于适合非线性过程建模、控制及优化的高效率全局优化算法的研究与开发。近年来,进化算法(Evolutionary Algorithm,EA)凭借其求解复杂非线性优化问题所表现出的全局搜索能力、通用性和易用性,在系统分析、模型辨识、控制器设计等领域得到了广泛应用。然而,进化算法存在寻优结果一致性差、局部搜索效率低、求解精度以及实时性不能令人满意等不足。针对上述问题,Memetic算法(Memeticalgorithm, MA)将基于全局的随机搜索和基于局部的启发式搜索有机地结合起来。该类算法采用进化算法的操作流程,引入局部启发式搜索来模拟由大量专业知识支撑的变异过程,在保证较高收敛性能的同时又能获得高质量解,大大提高了算法的搜索效率和求解精度。
本研究立足于非线性模型预测控制领域,对预测模型和滚动优化两个关键环节中存在的研究难点及其应用现状做了深入而全面的总结。基于最新提出的统计物理学概念——极值动力学,借鉴协同进化思想,在MEMETIC框架下提出了两种基于极值动力学的高效率混合优化算法,通过一系列典型优化问题验证了所提算法的有效性,并进一步将其应用于非线性预测控制的模型辨识和滚动优化中。本文的主要工作包括:
(1)针对现有的极值优化算法在理论基础、局部搜索能力方面存在的不足,对协同进化、Memetic算法以及极值动力学的内在联系进行了分析。基于Memetic算法的框架,将确定性局部搜索算法——列文伯格-马夸尔特算法(Levenberg–Marquardt,LM)作为局部搜索算子引入到极值优化算法(Extremal Optimization, EO)的搜索过程中,提出了一种用于求解无约束优化问题的混合EO-LM算法,并将其应用到典型的无约束优化问题——神经网络的学习中,取得了比传统神经网络学习算法更好的结果。
(2)在上述算法的基础上,考虑到预测控制中的优化问题常常带有各种约束,设计了可求解无约束/有约束非线性优化问题的EO-SQP算法,并通过一系列典型非线性数值优化问题的仿真验证了算法的有效性,进一步,结合仿真实例的优化过程对所提算法动力学特点进行了分析。与传统优化算法相比,所提出的EO-SQP算法具有良好的优化性能和求解效率。
(3)根据非线性预测控制中对预测模型的需求,提出了一种可对支持向量机模型核函数/参数同时优化的EO-SVR算法,并应用于非线性系统的动态建模;同时,采用可变窗口变异策略对EO-SQP算法进行改进,使之可用于NMPC的滚动优化。在上述预测模型和在线优化算法的基础上,设计NMPC控制器,并通过一类典型的非线性系统——连续混合反应釜的仿真,验证了所提算法的有效性。
(4)针对转炉炼钢这一典型的非线性工业生产过程,采用EO-LM算法对转炉炼钢终点质量神经网络预测模型的连接权值和偏置系数进行优化;并提出了一种基于EO-SQP的转炉炼钢配料调整算法。在此基础上,设计开发了转炉炼钢终点质量预测与控制系统,通过实际工业数据验证了本文所提算法的有效性和可行性。
As oneofthe mostpopular advancedprocesscontrol (APC) solutions,the model predictive control (MPC) has been widely applied in processindustries during recent years. Due to the rapid development of industryand technology, the control performance requirements for the large-scale,complicated and uncertain system keep rising. Basically, the issuesmentioned above mainly involve solving various types of complicatednonlinear optimization problems. The linear model predictive control(LMPC), which usually relies on a linear dynamic model, is inadequateforabovementionedproblemsandthelimitationbecomesmoreandmoreobvious. Nonlinear model predictive control (NMPC) appears to be aperspective method. However, the introduction of nonlinear predictivemodel brings some difficulties, such as the parameters/structure selectionof the nonlinear predictive model and the online receding horizonoptimization. All these issues encourage researchers and practitioners todevote themselves to the development of novel optimization methodswhich are suitable for the applications in nonlinear model predictivecontrol.
The evolutionary algorithms (EAs) have been widely used to solvemanycomplex nonlinear problems in control engineering,suchas systemanalysis, model identification and controller design, and shown theadvantages of global search efficiency, generality and practicality.However, due to the inherited shortcomings of Darwin’s theory, theevolution of computations often suffer from low local search capabilityand accuracy. In order to overcome these limitations,“MemeticAlgorithm”(MA) was proposed by incorporating the modern evolutiontheory and Dawkins’s“Meme”concept into evoluation computation. MAis a very efficient stochastic heuristics for global optimization, bycombining the global search nature of EA with local search to improve individualsolutions.
Thisthesisstartswiththegeneralreviewoftwokeyresearchissuesinnonlinear model predictive control: prediction model andrecedinghorizonoptimization. Base on the newly proposed concept called“Extremal dynamics”in statistical physics and co-evolution theory, weproposed two highly efficient optimization algorithms under theframework of MA, and then applied them to a number of benchmarkproblems, such as numerical optimizations, NMPC online optimizaitionand industrial applications. The main topics studied in this thesis aresummarizedasfollows:
(1)Regarding the disadvantages of current evolutionary computationin theoretical foundation and local search effectiveness, theinter-relations between co-evolution, memetic algorithm andextremal dynamics are studied. Anovel hybrid memetic algorithmwith the integration of“Extremal Optimization”and“Levenberg–Marquardt”is proposed, for unconstrained nonlinearoptimization. The proposed method is then employed for trainingof multilayer perceptron (MLP) networks and its effectiveness isdemonstrated.
(2)Considering the existence of constraints in optimal controlproblems, a novel EO-SQP algorithm is designed for theoptimization of nonlinear problems with/without constraints. Thesearch dynamics of the proposed methods are analysed in detail.The effectiveness and the efficiency of the proposed methods areproven by the comparison analysis on a number of benchmarknumericalNLPproblems.(3)For the reqirements of model identification and online receding
horizon optimization in NMPC, the extremal optimization isextendedandfurtherappliedtothesimultaneouslyoptimizationofSVR kernel functions/related parameters and the building ofdymamical model according to multi-step-ahead error. Then,based on the predictive model, a“Horizon based mutation”EO-SQP is designed and served as the online optimizer of aNMPC controller. Finally the simulation on a nonlinear MIMO CSTR is carried out to show the performance of the proposedalgorithm.
(4)For a typical nonlinear industrial process - basic oxygen furnaces(BOF) in steelmaking, the proposed neural network model basedon EO-LM learning algorithm is further applied to predict theend-pointproductquality;andtheEO-SQPalgorithmisemployedto find the optimal recipe adjustment. Then an integrated BOFproduction quality control system based on the above mentionedprediction model and optimal recipe adjustment algorithm isdeveloped. The effectiveness of the proposed algorithm is provenbythesimulationresultsonpracticalproductiondata.
本研究立足于非线性模型预测控制领域,对预测模型和滚动优化两个关键环节中存在的研究难点及其应用现状做了深入而全面的总结。基于最新提出的统计物理学概念——极值动力学,借鉴协同进化思想,在MEMETIC框架下提出了两种基于极值动力学的高效率混合优化算法,通过一系列典型优化问题验证了所提算法的有效性,并进一步将其应用于非线性预测控制的模型辨识和滚动优化中。本文的主要工作包括:
(1)针对现有的极值优化算法在理论基础、局部搜索能力方面存在的不足,对协同进化、Memetic算法以及极值动力学的内在联系进行了分析。基于Memetic算法的框架,将确定性局部搜索算法——列文伯格-马夸尔特算法(Levenberg–Marquardt,LM)作为局部搜索算子引入到极值优化算法(Extremal Optimization, EO)的搜索过程中,提出了一种用于求解无约束优化问题的混合EO-LM算法,并将其应用到典型的无约束优化问题——神经网络的学习中,取得了比传统神经网络学习算法更好的结果。
(2)在上述算法的基础上,考虑到预测控制中的优化问题常常带有各种约束,设计了可求解无约束/有约束非线性优化问题的EO-SQP算法,并通过一系列典型非线性数值优化问题的仿真验证了算法的有效性,进一步,结合仿真实例的优化过程对所提算法动力学特点进行了分析。与传统优化算法相比,所提出的EO-SQP算法具有良好的优化性能和求解效率。
(3)根据非线性预测控制中对预测模型的需求,提出了一种可对支持向量机模型核函数/参数同时优化的EO-SVR算法,并应用于非线性系统的动态建模;同时,采用可变窗口变异策略对EO-SQP算法进行改进,使之可用于NMPC的滚动优化。在上述预测模型和在线优化算法的基础上,设计NMPC控制器,并通过一类典型的非线性系统——连续混合反应釜的仿真,验证了所提算法的有效性。
(4)针对转炉炼钢这一典型的非线性工业生产过程,采用EO-LM算法对转炉炼钢终点质量神经网络预测模型的连接权值和偏置系数进行优化;并提出了一种基于EO-SQP的转炉炼钢配料调整算法。在此基础上,设计开发了转炉炼钢终点质量预测与控制系统,通过实际工业数据验证了本文所提算法的有效性和可行性。
As oneofthe mostpopular advancedprocesscontrol (APC) solutions,the model predictive control (MPC) has been widely applied in processindustries during recent years. Due to the rapid development of industryand technology, the control performance requirements for the large-scale,complicated and uncertain system keep rising. Basically, the issuesmentioned above mainly involve solving various types of complicatednonlinear optimization problems. The linear model predictive control(LMPC), which usually relies on a linear dynamic model, is inadequateforabovementionedproblemsandthelimitationbecomesmoreandmoreobvious. Nonlinear model predictive control (NMPC) appears to be aperspective method. However, the introduction of nonlinear predictivemodel brings some difficulties, such as the parameters/structure selectionof the nonlinear predictive model and the online receding horizonoptimization. All these issues encourage researchers and practitioners todevote themselves to the development of novel optimization methodswhich are suitable for the applications in nonlinear model predictivecontrol.
The evolutionary algorithms (EAs) have been widely used to solvemanycomplex nonlinear problems in control engineering,suchas systemanalysis, model identification and controller design, and shown theadvantages of global search efficiency, generality and practicality.However, due to the inherited shortcomings of Darwin’s theory, theevolution of computations often suffer from low local search capabilityand accuracy. In order to overcome these limitations,“MemeticAlgorithm”(MA) was proposed by incorporating the modern evolutiontheory and Dawkins’s“Meme”concept into evoluation computation. MAis a very efficient stochastic heuristics for global optimization, bycombining the global search nature of EA with local search to improve individualsolutions.
Thisthesisstartswiththegeneralreviewoftwokeyresearchissuesinnonlinear model predictive control: prediction model andrecedinghorizonoptimization. Base on the newly proposed concept called“Extremal dynamics”in statistical physics and co-evolution theory, weproposed two highly efficient optimization algorithms under theframework of MA, and then applied them to a number of benchmarkproblems, such as numerical optimizations, NMPC online optimizaitionand industrial applications. The main topics studied in this thesis aresummarizedasfollows:
(1)Regarding the disadvantages of current evolutionary computationin theoretical foundation and local search effectiveness, theinter-relations between co-evolution, memetic algorithm andextremal dynamics are studied. Anovel hybrid memetic algorithmwith the integration of“Extremal Optimization”and“Levenberg–Marquardt”is proposed, for unconstrained nonlinearoptimization. The proposed method is then employed for trainingof multilayer perceptron (MLP) networks and its effectiveness isdemonstrated.
(2)Considering the existence of constraints in optimal controlproblems, a novel EO-SQP algorithm is designed for theoptimization of nonlinear problems with/without constraints. Thesearch dynamics of the proposed methods are analysed in detail.The effectiveness and the efficiency of the proposed methods areproven by the comparison analysis on a number of benchmarknumericalNLPproblems.(3)For the reqirements of model identification and online receding
horizon optimization in NMPC, the extremal optimization isextendedandfurtherappliedtothesimultaneouslyoptimizationofSVR kernel functions/related parameters and the building ofdymamical model according to multi-step-ahead error. Then,based on the predictive model, a“Horizon based mutation”EO-SQP is designed and served as the online optimizer of aNMPC controller. Finally the simulation on a nonlinear MIMO CSTR is carried out to show the performance of the proposedalgorithm.
(4)For a typical nonlinear industrial process - basic oxygen furnaces(BOF) in steelmaking, the proposed neural network model basedon EO-LM learning algorithm is further applied to predict theend-pointproductquality;andtheEO-SQPalgorithmisemployedto find the optimal recipe adjustment. Then an integrated BOFproduction quality control system based on the above mentionedprediction model and optimal recipe adjustment algorithm isdeveloped. The effectiveness of the proposed algorithm is provenbythesimulationresultsonpracticalproductiondata.
引文
[1]Richalet, J., Rault, A., Testud, J. L., Papon, J., Modelpredictiveheuristic control: applicationstoindustrial processes.Automatica,1978,14(5):413-428.
[2] Culter, C. R., Ramaker, B. L., Dynamic matrix control-a computer control algorithm.ProceedingsJointAutomaticControlConference,1980.
[3] Rouhani, R., Mehra, R. K., Model algorithmic control (MAC): basic theoretical properties.Automatica, 1982,18(4):401-414.
[4] Garcia, C. E., Morari, M., Internal model control: a unifying review and some new results.Industrial&EngineeringChemistryProcessDesignandDevelopment,1982,21(2):308-323.
[5] Clarke, D. W., Montadi, C., Tuffs, P. S., Generalized predictive control. Automatica, 1987,23(2):137-162.
[6] Clarke, D. W., Application of generalized predictive control to industrial processes. IEEEControlSystemsMagazine, 1988,8(2):49-55.
[7]AlSeyab,R. K.,Cao,Y.,NonlinearmodelpredictivecontrolfortheALSTOMgasifier. JournalofProcessControl,2006,16(8):795-808.
[8] Henson, M. A., Nonlinear model predictive control: current status and future directions.ComputersandChemicalEngineering, 1998,23(2):187-202.
[9] Morari, M., Lee, J. H., Model predictive control: past, present and future. Computers andChemicalEngineering,1999,23(4-5):667-682.
[10] Qin, S. J., Badgwell, T. A., A survey of industrial model predictive control technology.ControlEngineeringPractice,2003,11(7):733-764.
[11] Kouvaritakis, B., Cannon, M., Nonlinear Predictive Control: Theory and Practice. London:InstitutionofElectricalEngineers,2001.
[12] Camacho, E. F., Bordons, C., Nonlinear model predictive control: an introductory review.AssessmentandFutureDirectionsofNonlinearModelPredictiveControl,2007,358:1-16.
[13]孙峻.非线性模型预测控制理论及应用研究.博士学位论文,西北工业大学.2002.
[14] Zadeh, L. A., From circuit theory to system theory. Proceedings of the IRE, 1962,50(5):856-865.
[15]朱耀春.基于基因表达式编程技术的非线性系统辨识研究.博士学位论文,华北电力大学.2008.
[16] Fleming, P. J., Purshouse, R. C., Evolutionary algorithms in control systems engineering: asurvey.ControlEngineeringPractice,2002,10(11):1223-1241.
[17]Zadeh,L.A., Fuzzyalgorithm.InformationandControl,1968,94-102.
[18] Zadeh, L.A., Outline of a new approach to the analysis of complex systems and decisionprocesses.IEEETransactionsonSystemsManCybernet, 1973,3(1):28-44.
[19]Mamdani,E.H.,Applicationoffuzzylogictoapproximatereasoningusinglinguisticsystems.IEEETransactionsonComputers,1977,26(12):1182-1191.
[20] Hellendoorn, H., Driankov, D., Fuzzy Model Identification: Selected Approaches. Berlin:Springer, 1997.
[21]Vapnik,V.,TheNatureofStatisticalLearningTheory.NewYork: Springer-Verlag,1995.
[22]Burges, C. J. C., Atutorial on support vector machines for pattern recognition. Data MiningandKnowledgeDiscovery,1998,2(2):121-167.
[23]Sj?berg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P.Y., Hjalmarsson,H., Juditsky, A., Nonlinear black-box modeling in system identification: a unified overview.Automatica.1995,31(12):1691-1724.
[24]Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V.,Support Vector RegressionMachines.AdvancesinNeuralInformationProcessingSystems.1997,1:155-161.
[25] Kristinsson, K., Dumont, G. A., System identification and control using genetic algorithms.IEEETransactionsonSystems,Man,andCybernetics, 1992,22(5):1033-1046.
[26] Hunt, K. J., Sbarbaro, D., Zbibowski, R., Gawthrop, P. J., Neural networks for controlsystems-asurvey.Automatica,1992,28(6):1083-1111.
[27] Willis, M.J., Montague, G. A., Di Massimo, C., Tham, M. T., Artificial neural networks inprocessestimationandcontrol.Automatica,1992,28(6):1181-1187.
[28]Gupta,P., Sinha,N.K., Neuralnetworksforidentificationof nonlinearsystems: anoverview.SoftComputingandIntelligentSystems,2000,337-356.
[29] Du, H. P., Zhang, N., Application of evolving Takagi–Sugeno fuzzy model to nonlinearsystemidentification.AppliedSoftComputing,2008,8(1):676–686.
[30] Donat, J.S., Neural Net Based Model Predictive Control. International Journal of Control.1991,54(6):1453-1468.
[31] Mills, P.M., Zomaya, A.Y., Tade, M.O., Adaptive Model-Based Control Using NeuralNetworks.InternationalJournalofControl.1994,60(6):1163-1192.
[32]李少远,刘浩,袁著祉.基于神经网络误差修正的广义预测控制.控制理论与应用.1996,13(5):677-680.
[33]Piche, S., Sayyar-Rodsari, B., Johnson, D., Gerules, M., Nonlinear model predictive controlusingneuralnetworks.IEEEControlSystems, 2000,20(3):53-62.
[34] Miao, Q., Wang, S.F., Nonlinear Modeling predictive control based on support vectorregression.InternationalConferenceonMachineLearningandCybernetics.2002,3:1657-1661.
[35]Biegler,L.T., Grossmann,I.E.,Westerberg,A.W.,SystematicMethodsofChemicalProeessDesign. NJ:PrenticeHall,1997.
[36]Wiener,N.,CyberneticsorControlandCommunicationintheAnimalandtheMachine.NewYork:MIT,1948.
[37]Tsien,H. S.,EngineeringCybernetics.NewYork:McGrawHill,1954.
[38] Bellman, R., Functional equations in the theory of dynamic programming V: positivity andquasi-linearity. ProceedingsoftheNationalAcademyofSciences,1955,41(10):743-746.
[39] Pontryagin, L. S., Boltayanskii, V. G., Gamkrelidze, R. V., Mishchenko, E. F., Themathematicaltheoryofoptimalprocesses.JohnWiley&Sons, 1962.
[40] Kalman, R.E., A new approach to linear filtering and prediction problems. Journal of BasicEngineering,1960,82(1):35-45.
[41]何德峰.约束非线性预测控制算法及其鲁棒稳定性研究.博士学位论文,中国科学技术大学.2008.
[42]Maciejowski,J.M.,PredictiveControlwithConstraints.UK:PrenticeHall,2002.
[43] Camacho, E. F., Bordons, C., Model predictive control in the process industry. Berlin:SpringerVerlag,1995.
[44]陈薇.非线性预测控制快速算法的研究与应用.博士学位论文,中国科学技术大学.2007.
[45]Onnen,C.,Babuska, R., Kaymak, U., Sousa,J. M.,Verbruggen,H. B., Isermann, R.,Geneticalgorithms for optimization in predictive control. Control Engineering Practice, 1997, 5(10),1363-1372.
[46] Martinez, M., Senent, J. S., Blasco, X., Generalized predictive control using geneticalgorithms(GAGPC).EngineeringApplicationsofArtificialIntelligence,1998,11(3):355-367.
[47] Potocnik, P., Grabec, I., Nonlinear model predictive control of a cutting process.Neurocomputing,2002,43(1-4):107-126.
[48] Song, Y., Chen, Z., Yuan, Z., New chaotic PSO-based neural network predictive control fornonlinearprocess.IEEETransactionsonNeuralNetworks,2007,18(2):595-601.
[49]Venkateswarlu, Ch., Reddy, A. D., Nonlinear model predictive control of reactive distillationbased on stochastic optimization. Industrial and Engineering Chemistry Research, 2008,47(18):6949-6960.
[50] Boettcher, S., Percus, A. G., Extremal optimization: methods derived from co-evolution.ProceedingsoftheGeneticandEvolutionaryComputationConference,1999,825-832.
[51] Bak, P., Tang, C., Wiesenfeld, K., Self-organized criticality: an explanation of 1/f noise.PhysicalReviewLetter,1987,59(4):381-384.
[52]Bak, P., How nature works: the science of self-organised criticality. Berlin: Springer-Verlag,1996.
[53] Malamud, B. D., Morein, G., Turcotte, D. L., Forest fires: an example of self-organizedcriticalbehavior.Science, 1998,281(5384):1840-1842.
[54] Smethurst, D. P., Williams, H. C., Are hospital waiting lists self-regulating? Nature, 2001,410:652-653.
[55] Bhattacharya, K., Manna, S. S., Self-organized critical models of earthquakes. Physica A:StatisticalMechanicsanditsApplications,2007,384(1):15-20.
[56]Chen,Y.,Zhou,Y.,Scalinglawsandindicationsofself-organizedcriticalityinurbansystems.Chaos,SolitonsandFractals,2008, 35:85–98.
[57] Bak, P., Sneppen, K., Punctuated equilibrium and criticality in a simple model of evolution.PhysicalReviewLetter,1993,71(24):4083-4086.
[58]陈泯融,基于极值动力学的优化方法及其应用研究.博士学位论文,上海交通大学.2008.
[59]Boettcher,S.,Percus,A. G.,Extremaloptimizationfor graph partitioning.PhysicalReviewE,2001,64(2):1-13.
[60]Menai, M. E., Batouche, M.,Efficient initial solution toextremal optimization algorithm forweightedMAXSATproblem.LectureNotesinComputerScience, 2003,2718:319-338.
[61] Boettcher, S., Percus, A. G., Extremal optimization at the phase transition of thethree-coloring problem.PhysicalReviewE,2004,69(6):1-8.
[62] Chen, Y. W., Lu, Y. Z., Chen, P., Optimization with extremal dynamics for the travelingsalesmanproblem.PhysicaA:StatisticalMechanicsanditsApplications,2007,1(1):115-123.
[63]Chen, M. R., Lu, Y. Z., Anovel elitist multiobjective optimization algorithm: multiobjectiveextremaloptimization.EuropeanJournalofOperationalResearch,2008,188(3):637-651.
[64] Chen, P., Lu, Y. Z., Chen, Y. W., Extremal optimization combined with LM gradient searchfor MLP network learning. International Journal of Computational Intelligence Systems, 2010,3(5):622-631.
[65]Chen, P., Lu, Y. Z., Nonlinear modelpredictive control with the integration ofsupport vectormachine andextremaloptimization.Prodceedingofthe8thWorld Congresson IntelligentControlandAutomation,2010,3167-3172.
[66]Dawkins,R.,TheSelfishGene.Oxford:OxfordUniversityPress,1976.
[67]Moscato,P.,On evolution,search, optimization,genetic algorithms, and martial arts:towardsmemetic algorithms. California Institute of Technology, Pasadena, 1989, Caltech ConcurrentComputationProgram,Report.826.
[68] Cotta, C., Moscato, P., Memetic algorithms. Handbook of Approximation Algorithms andMetaheuristics. NewYork:CRCPress,2007.
[69]Wolpert,D.H.,Macready,W.G., Nofreelunchtheoremsforoptimization.IEEETransactionsonEvolutionaryComputation,1997,1(1):67-82.
[70]段海滨,张祥银,徐春芳,仿生智能计算.北京:科学出版社,2011.
[71] Digalakis, J., Margaritis, K., Performance comparison of memetic algorithms. AppliedMathematicsandComputation,2004,158(1):237-252.
[72]Ozcan, E., Memes, self-generation and nurse rostering. Lecture Notes in Computer Science,2007,3867:85-104.
[73]Lü, Z., Hao, J. K., Amemetic algorithm for graph coloring. European Journal ofOperationalResearch,2010,203(1):241-250.
[74] Nagata, Y., Braysy, O., Dullaert, W., A penalty-based edge assembly memetic algorithm forthe vehicle routing problem with time windows. Computers & Operations Research,2010, 37(4):724-737.
[75]Krasnogor, N.,Gustafson, S., Astudyon the use of“self-generation”in memetic algorithms.NaturalComputing,2004,3(1):53-76.
[76] Zhu, Z., Ong, Y. S., Zurada, J. M., Identification of full and partial class relevant genes.IEEE/ACMTransactionsonComputationalBiologyandBioinformatics,2010,7(2):263-277.
[77] Pacsuski, M., Maslov, S., Bak, P., Avalanche dynamics in evolution, growth, and depiningmodels.PhysicalReviewE, 1996,53(1):414-443.
[78] Sneppen, K., Extremal dynamics and punctuated co-evolution. Physica A: Statistical andTheoreticalPhysics,1995,221(1-3):168-179.
[79]杨根科,陈玉旺,戴博,系统工程中自组织优化方法及其应用.系统工程理论与实践,2008,(6):142-147.
[80]刘静,协同进化算法及其应用研究.博士学位论文,西安电子科技大学.2004.
[81] Ehrlich, P. R., Raven, P. H., Butterflies and plants: a study in coevolution. Society for theStudyofEvolution.1964,18(4):586-608.
[82]Haken,H.,Synergetics.Berlin:Springer, 1977.
[83]Jazen,D.H.,Whenisit coevolution.Evolution, 1980,34(3):611-612.
[84]李碧,协同进化算法的研究及其应用.博士学位论文,华南理工大学,2010.
[85] Smith, J. E., Coevolving memetic algorithms: a review and progress report. IEEETransactionsonSystems,Man,andCybernetics,PartB:Cybernetics,2007,37(1):6-17.
[86]梅可玉,论自组织临界性与复杂系统的演化行为.自然辩证法研究,2004,20(7):6-9.
[87] Levenberg, K., A method for the solution of certain nonlinear problems in least squares.QuarterlyofAppliedMathematics.1944,2:164-166.
[88]Marquardt, D.W.,An algorithm for least-squares estimation of nonlinear inequalities. SIAMJournalonAppliedMathematics.1963,11(2):431-441.
[89]Nocedal,J.,Wright,S.J.,Numericaloptimization.NewYork:Springer, 1999.
[90]Hagan, M. T., Menhaj, M. B., Training feedforward networks with the Marquardt algorithm.IEEETransactionsonNeuralNetworks,1994,5(6):989-993.
[91]Sousa, F. L., Vlassov, V., Ramos, F. M., Generalized extremal optimization: an application inheatpipedesign.AppliedMathematicalModelling,2004,28(10):911-931.
[92]Haykin,S., NeuralNetworks:AComprehensiveFoundation.NewYork:Macmillan, 1994.
[93] Werbos, P., Beyond Regression: New Tools for Prediction and Analysis in the BehavioralSciences.Ph.D.Thesis,HarvardUniversity,1974.
[94] Rumelhart, D. E., Hinton, G. E., Williams, R. J., Learning representations by backpropagatingerrors.Nature,1986a,323(9):533-536.
[95]Rumelhart, D. E., Hinton, G. E., Williams, R. J., Learning InternalRepresentations by ErrorPropagation.MA:MITPress,1986b,318-362.
[96]Salomon, R.,Evolutionaryalgorithms and gradient search: similarities and differences. IEEETransactionsonEvolutionaryComputation,1998,2(2):45-55.
[97] Jacobs, R. A., Increased rates of convergence through learning rate adaptation. NeuralNetworks, 1988,1(4):295-307.
[98]Rigler, A. K., Irvine, J. M., Vogl, T. P., Rescaling of variables in back propagation learning.NeuralNetworks,1991,4(2):225-229.
[99]Fukuoka,Y.,Matsuki,H.,Minamitani,H., Ishida,A.,Amodifiedbackpropagationmethodtoavoidfalselocalminima.NeuralNetworks, 1998,11(6):1059-1072.
[100] Hush, D. R., Training a sigmoidal node is hard. Neural Computation, 1999,11(5):1249-1260.
[101] Engelbrecht, A. P., Computational intelligence: an introduction. New York: John Wiley &Sons,2007.
[102] Arifovic, J., Gencay, R., Using genetic algorithms to select architecture of a feedforwardartificial neural network. Physica A: Statistical Mechanics and its Applications, 2001, 289(3-4):574-594.
[103] Li, S. J., Li, Y., Liu, Y., Xu, Y. F., A GA-based NN approach for makespan estimation.AppliedMathematicsand Computation, 2007a,185(2):1003-1014.
[104]Yao, X., Islam, Md. M.,Evolving artificial neural network ensembles. IEEE ComputationalIntelligenceMagazine,2008,3(1):31-42.
[105] Dengiz, B., Alabas-Uslu, C., Dengiz, O.,Atabu search algorithm for the training of neuralNetworks.JournaloftheOperationalResearchSociety,2009,60(2):282-291.
[106] Fasih, A., Chedjou, C. J., Kyamakya, K.,Cellular neural networks-based genetic algorithmfor optimizing the behavior of an unstructured robot. International Journal of ComputationalIntelligenceSystems,2009,124-133.
[107] Reyaz-Ahmed, A., Zhang Y. Q., Harrison, R. W., Granular decision tree and evolutionaryneural SVM for protein secondary structure prediction. International Journal of ComputationalIntelligenceSystems,2009,2(4):343-352.
[108] Ackley, D. H., A connectionist machine forgenetic hill climbing. Boston: Kluwer Boston,1987.
[109] Martinsen, F., Biegler, L. T., Foss, B. A., Anew optimization algorithm with application tononlinearMPC.JournalofProcessControl,2004,14(8):853-865.
[110]Wang, Y., Cai, Z., Zhou, Y., Fan, Z., Constrained optimization based on hybrid evolutionaryalgorithm and adaptive constraint-handling technique. Structural and MultidisciplinaryOptimization,2009,37:395-413.
[111]Handoko,S.D.,Kwoh,C.K.,Ong,Y. S.,Lim,M.H.,Astudyon constrainedMAusingGAand SQP: analytical vs. finite-differencegradients. Proceedings of the Congress on EvolutionaryComputation,2008,4031-4038.
[112] Vargas, M. C., Torres-Jimenez, J., Ruiz-Suarez, J. C., Analysis of X-ray diffraction datausing a hybrid stochastic optimization method. Journal of Physics A: Mathematical and General,2002,35:3865-3876.
[113] Runarsson, T. P., Yao, X., Stochastic ranking for constrained evolutionary optimization.IEEETransactionsonEvolutionaryComputation,2000,4:284-294.
[114]Zhang, M., Luo, W., Wang, X., Differential evolution with dynamic stochastic selection forconstrainedoptimization.InformationSciences, 2008,178:3043-3074.
[115] Wilson, R. B., ASimplicial Algorithm for Concave Programming. Ph. D. Thesis, HarvardUniversity,1963.
[116] Han, S. P., Superlinearly convergent variable metric algorithms for general nonlinearprogrammingproblems.MathematicalProgramming,1976,11:263-282.
[117] Powell, M. J. D., A fast algorithm for nonlinearly constrained optimization calculations.LectureNotesinMathematics, 1978,630:144-157.
[118] Moscato, P., Cotta, C., A gentle introduction to memetic algorithms. Handbook ofMetaheuristics.Boston: KluwerAcademicPublishers, 2003,105-143.
[119] Martin, O. C., Monasson, R., Zecchina, R., Statistical mechanics methods and phasetransitionsinoptimizationproblems.TheoreticalComputerScience, 2001,265(1-2):3-67.
[120] Murty, K. G., Kabadi, S. N., Some NP complete problems in quadratic and nonlinearprogramming,MathematicalProgramming,1987, 39:117-129.
[121]Fukumizu, K., Amari, S.,Local minima and plateausin hierarchicalstructures ofmultilayerperceptrons.NeuralNetworks,2000,13(3):317-327.
[122] Mezura-Montes, E., Carlos, A. C. C., Asimple multimemberedevolution strategy to solveconstrained optimization problems. IEEE Transactions on Evolutionary Computation, 2005,9:1-17.
[123] Zhu, W., Ali, M. M., Solving nonlinearly constrained global optimization problem via anauxiliary function method. Journal of Computational and Applied Mathematics, 2009,230:491-503.
[124]席裕庚.预测控制.北京:国防工业出版社.1993.
[125] Na, M.G., Upadhyaya, B.R., Model Predictive Control of an SP-100 Space Reactor UsingSupportVectorRegressionandGeneticOptimization.IEEETransactionsonNuclearScience.2006,53(4):2318-2327.
[126]Ong,C.J.,Sui,D.,Gilbert,E.G.,Enlargingtheterminalregionofnonlinearmodelpredictivecontrolusingthesupportvectormachinemethod.Automatica.2006,42(6):1011-1016.
[127] Xi, X.C., Poo, A.N., Chou, S.K., Support vector regression model predictive control on aHVACplant.ControlEngineeringPractice.2007,15(8):897-908.
[128] Li, L.J., Su, H.Y., Chu, J., Generalized Predictive Control with Online Least SquaresSupportVectorMachines.ActaAutomaticaSinica.2007b,33(11):1182-1188.
[129]Zhang,R.D.,Wang,S.Q., Supportvectormachinebasedpredictivefunctionalcontroldesignforoutputtemperatureofcokingfurnace.JournalofProcessControl.2008,18(5):439-448.
[130] Forssell, N., Ljung, L., Closed-loop identification revisited. Automatica, 1999,35(7):1215-1241.
[131] Steve, G., Support vector machines classification and regression. Image, Speech, andIntelligentSystemsGroup,UniversityofSouthampton,UK,1998,ISISTechnicalReport.
[132] Min, J. H., Lee, Y. C., Bankruptcy prediction using support vector machine with optimalchoiceofkernelfunctionparameters.ExpertSystemswithApplications, 2005,28(4):603-614.
[133] Ali, S., Smith, K. A., A meta-learning approach to automatic kernel selection for supportvectormachines.Neurocomputing, 2006,70(1-3):173-186.
[134] Jeng, J. T., Hybrid approach of selecting hyperparameters of support vector machine forregression. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2006,36(3):699-709.
[135] Hou, S. M., Li, Y. R., Short-term fault prediction based on support vector machines withparameter optimization by evolution strategy. Expert Systems with Applications, 2009,36(10):12383-12391.
[136]Lorena, A.C., De Carvalho, A.,Evolutionarytuning of SVM parameter values in multiclassproblems.Neurocomputing,2008,71(16-18):3326-3334.
[137]Zhang,X.L.,Chen,X. F.,He,Z.J.,AnACO-basedalgorithmforparameteroptimizationofsupportvectormachines.ExpertSystemswithApplications,2010,37(9):6618-6628.
[138] Hsu, C. W., Chang, C. C., Lin, C. J., A practical guide to support vector classification.NationalTaiwanUniversity,Taiwan,2004, Report.
[139] Friedrichs, F., Igel, C., Evolutionary tuning of multiple SVM parameters. Neurocomputing,2005,64:107-117.
[140]Avci, E., Selectingofthe optimalfeature subsetand kernelparametersin digitalmodulationclassification by using hybrid genetic algorithm-support vector machines: HGASVM. ExpertSystemswithApplications, 2009,36(2):1391-1402.
[141] Pai, P. F., Hong, W. C., Support vector machines with simulated annealing algorithms inelectricityloadforecasting.EnergyConversionandManagement,2005,46(17):2669-2688.
[142] Mao, Y., Zhou, X., Pi, D., Sun, Y., Wong, S. T.C., Parameters selection in gene selectionusing Gaussian kernel support vector machines by genetic algorithm, Journal of ZhejiangUniversity-ScienceB,2005,6(10):961-973.
[143] Wu, C. H., Tzeng, G. H., Goo, Y. J., Fang, W. C., A real-valued genetic algorithm tooptimizetheparametersofsupportvectormachineforpredictingbankruptcy.ExpertSystemswithApplications, 2007,32(2):397-408.
[144] Tang, X., Zhuang, L., Jiang, C., Prediction of silicon content in hot metal using supportvector regression based on chaos particle swarm optimization. Expert Systems with Applications,2009,36(9):11853-11857.
[145] Saini, L. M., Aggarwal, S. K., Kumar, A., Parameter optimization using genetic algorithmfor support vector machine-based price-forecasting model in National electricity market. IETGeneration,Transmission&Distribution,2010,4(1):36-49.
[146]Wu, Q., Ahybrid-forecasting model based on Gaussian support vector machine and chaoticparticleswarmoptimization.ExpertSystemswithApplications,2010,37(3):2388-2394.
[147] Howley, T., Madden, M. G., The genetic kernel support vector machine: description andevaluation.ArtificialIntelligenceReview,2005,24(3-4):379-395.
[148] Thadani, K., Jayaraman, A., Sundararajan, V., Evolutionary selection of kernels in supportvector machines. International Conference on Advanced Computing and Communications, 2006,19-24.
[149] Wu, C. H., Tzeng, G. H., Lin, R. H., A novel hybrid genetic algorithm for kernel functionand parameteroptimization in supportvectorregression. ExpertSystemswith Applications,2009,36(3):4725-4735.
[150]Chen,P., Lu, Y. Z., Extremal optimization for optimizing kernel function and its parametersinsupportvectorregression.JournalofZhejiangUniversity-SCIENCEC,2011,12(4):297-306.
[151]Chuang,C.C.,Lee,Z. J.,Hybridrobustsupportvectormachinesforregressionwithoutliers.AppliedSoftComputing, 2011,11(1):64-72.
[152] Qiao, J. F., Wang, H. D., A self-organizing fuzzy neural network and its applications tofunctionapproximationandforecastmodeling.Neurocomputing,2008,71(4-6):564-569.
[153] Narendra, K. S., Parthasarathy, K., Identification and control of dynamical systems usingneuralnetworks.IEEETransactionsonNeuralNetworks, 1990,1(1):4-27.
[154] Pearson, R. K., Nonlinear empirical modeling techniques. Computers and ChemicalEngineering,2006,30:1514-1528.
[155] Matsuura, T., Kato, M., Concentration stability of the isothermal reactor. ChemicalEngineeringScience,1967,22(2):171-183.
[156] Li, W. C., Biegler, L. T., Process control strategies for constrained nonlinear systems.IndustrialandEngineeringChemistryResearch,1988,27(8):1421-1433.
[157] Chen, P., Lu, Y. Z., Memetic algorithm-based neural network learning for basic oxygenfurnaceendpointprediction.JournalofZhejiangUniversity-SCIENCEA,2010,11(11):841-848.
[158] Cox, I. J., Lewis, R. W., Ransing, R.S., Laszczewski, H., Berni, G., Application of neuralcomputing in basic oxygen steelmaking. Journal of Materials Processing Technology, 2002,120(1-3):310-315.
[159] Wang, X., Wang, Z. J., Tao, J., Multiple neural network modeling method for carbon andtemperature estimation in basic oxygen furnace. Lecture Notes in Computer Science, 2006,3973:870-875.
[160] Tao, J., Wang, X., Chai, T. Y., Intelligent control method and application for BOFsteelmaking.ProceedingsoftheIFACWorldCongress,2002,1071-1076.
[161]Fileti, A. M. F., Pacianotto, T. A., Cunha, A. P., Neuralmodelling helps the BOSprocess toachieve aimed end-point conditions in liquid steel. Engineering Applications of ArtificialIntelligence,2006,19(1):9-17.
[162] Feng, M. X., Li, Q., Zou, Z. S., An outlier identification and judgment method for animproved neural-network BOF forecasting model. Steel Research International, 2008,79(5):323-332.
[163]Pendharkar, P. C., Genetic algorithm based neural network approaches for predicting churnincellularwirelessnetworkservices.ExpertSystemswithApplications,2009,36(3):6714-6720.
[164] Sedki, A., Ouazar, D., EI Mazoudi, E., Evolving neural network using real coded geneticalgorithm for daily rainfall-runoff forecasting. Expert Systems with Applications, 2009,36(3):4523-4527.
[2] Culter, C. R., Ramaker, B. L., Dynamic matrix control-a computer control algorithm.ProceedingsJointAutomaticControlConference,1980.
[3] Rouhani, R., Mehra, R. K., Model algorithmic control (MAC): basic theoretical properties.Automatica, 1982,18(4):401-414.
[4] Garcia, C. E., Morari, M., Internal model control: a unifying review and some new results.Industrial&EngineeringChemistryProcessDesignandDevelopment,1982,21(2):308-323.
[5] Clarke, D. W., Montadi, C., Tuffs, P. S., Generalized predictive control. Automatica, 1987,23(2):137-162.
[6] Clarke, D. W., Application of generalized predictive control to industrial processes. IEEEControlSystemsMagazine, 1988,8(2):49-55.
[7]AlSeyab,R. K.,Cao,Y.,NonlinearmodelpredictivecontrolfortheALSTOMgasifier. JournalofProcessControl,2006,16(8):795-808.
[8] Henson, M. A., Nonlinear model predictive control: current status and future directions.ComputersandChemicalEngineering, 1998,23(2):187-202.
[9] Morari, M., Lee, J. H., Model predictive control: past, present and future. Computers andChemicalEngineering,1999,23(4-5):667-682.
[10] Qin, S. J., Badgwell, T. A., A survey of industrial model predictive control technology.ControlEngineeringPractice,2003,11(7):733-764.
[11] Kouvaritakis, B., Cannon, M., Nonlinear Predictive Control: Theory and Practice. London:InstitutionofElectricalEngineers,2001.
[12] Camacho, E. F., Bordons, C., Nonlinear model predictive control: an introductory review.AssessmentandFutureDirectionsofNonlinearModelPredictiveControl,2007,358:1-16.
[13]孙峻.非线性模型预测控制理论及应用研究.博士学位论文,西北工业大学.2002.
[14] Zadeh, L. A., From circuit theory to system theory. Proceedings of the IRE, 1962,50(5):856-865.
[15]朱耀春.基于基因表达式编程技术的非线性系统辨识研究.博士学位论文,华北电力大学.2008.
[16] Fleming, P. J., Purshouse, R. C., Evolutionary algorithms in control systems engineering: asurvey.ControlEngineeringPractice,2002,10(11):1223-1241.
[17]Zadeh,L.A., Fuzzyalgorithm.InformationandControl,1968,94-102.
[18] Zadeh, L.A., Outline of a new approach to the analysis of complex systems and decisionprocesses.IEEETransactionsonSystemsManCybernet, 1973,3(1):28-44.
[19]Mamdani,E.H.,Applicationoffuzzylogictoapproximatereasoningusinglinguisticsystems.IEEETransactionsonComputers,1977,26(12):1182-1191.
[20] Hellendoorn, H., Driankov, D., Fuzzy Model Identification: Selected Approaches. Berlin:Springer, 1997.
[21]Vapnik,V.,TheNatureofStatisticalLearningTheory.NewYork: Springer-Verlag,1995.
[22]Burges, C. J. C., Atutorial on support vector machines for pattern recognition. Data MiningandKnowledgeDiscovery,1998,2(2):121-167.
[23]Sj?berg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P.Y., Hjalmarsson,H., Juditsky, A., Nonlinear black-box modeling in system identification: a unified overview.Automatica.1995,31(12):1691-1724.
[24]Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V.,Support Vector RegressionMachines.AdvancesinNeuralInformationProcessingSystems.1997,1:155-161.
[25] Kristinsson, K., Dumont, G. A., System identification and control using genetic algorithms.IEEETransactionsonSystems,Man,andCybernetics, 1992,22(5):1033-1046.
[26] Hunt, K. J., Sbarbaro, D., Zbibowski, R., Gawthrop, P. J., Neural networks for controlsystems-asurvey.Automatica,1992,28(6):1083-1111.
[27] Willis, M.J., Montague, G. A., Di Massimo, C., Tham, M. T., Artificial neural networks inprocessestimationandcontrol.Automatica,1992,28(6):1181-1187.
[28]Gupta,P., Sinha,N.K., Neuralnetworksforidentificationof nonlinearsystems: anoverview.SoftComputingandIntelligentSystems,2000,337-356.
[29] Du, H. P., Zhang, N., Application of evolving Takagi–Sugeno fuzzy model to nonlinearsystemidentification.AppliedSoftComputing,2008,8(1):676–686.
[30] Donat, J.S., Neural Net Based Model Predictive Control. International Journal of Control.1991,54(6):1453-1468.
[31] Mills, P.M., Zomaya, A.Y., Tade, M.O., Adaptive Model-Based Control Using NeuralNetworks.InternationalJournalofControl.1994,60(6):1163-1192.
[32]李少远,刘浩,袁著祉.基于神经网络误差修正的广义预测控制.控制理论与应用.1996,13(5):677-680.
[33]Piche, S., Sayyar-Rodsari, B., Johnson, D., Gerules, M., Nonlinear model predictive controlusingneuralnetworks.IEEEControlSystems, 2000,20(3):53-62.
[34] Miao, Q., Wang, S.F., Nonlinear Modeling predictive control based on support vectorregression.InternationalConferenceonMachineLearningandCybernetics.2002,3:1657-1661.
[35]Biegler,L.T., Grossmann,I.E.,Westerberg,A.W.,SystematicMethodsofChemicalProeessDesign. NJ:PrenticeHall,1997.
[36]Wiener,N.,CyberneticsorControlandCommunicationintheAnimalandtheMachine.NewYork:MIT,1948.
[37]Tsien,H. S.,EngineeringCybernetics.NewYork:McGrawHill,1954.
[38] Bellman, R., Functional equations in the theory of dynamic programming V: positivity andquasi-linearity. ProceedingsoftheNationalAcademyofSciences,1955,41(10):743-746.
[39] Pontryagin, L. S., Boltayanskii, V. G., Gamkrelidze, R. V., Mishchenko, E. F., Themathematicaltheoryofoptimalprocesses.JohnWiley&Sons, 1962.
[40] Kalman, R.E., A new approach to linear filtering and prediction problems. Journal of BasicEngineering,1960,82(1):35-45.
[41]何德峰.约束非线性预测控制算法及其鲁棒稳定性研究.博士学位论文,中国科学技术大学.2008.
[42]Maciejowski,J.M.,PredictiveControlwithConstraints.UK:PrenticeHall,2002.
[43] Camacho, E. F., Bordons, C., Model predictive control in the process industry. Berlin:SpringerVerlag,1995.
[44]陈薇.非线性预测控制快速算法的研究与应用.博士学位论文,中国科学技术大学.2007.
[45]Onnen,C.,Babuska, R., Kaymak, U., Sousa,J. M.,Verbruggen,H. B., Isermann, R.,Geneticalgorithms for optimization in predictive control. Control Engineering Practice, 1997, 5(10),1363-1372.
[46] Martinez, M., Senent, J. S., Blasco, X., Generalized predictive control using geneticalgorithms(GAGPC).EngineeringApplicationsofArtificialIntelligence,1998,11(3):355-367.
[47] Potocnik, P., Grabec, I., Nonlinear model predictive control of a cutting process.Neurocomputing,2002,43(1-4):107-126.
[48] Song, Y., Chen, Z., Yuan, Z., New chaotic PSO-based neural network predictive control fornonlinearprocess.IEEETransactionsonNeuralNetworks,2007,18(2):595-601.
[49]Venkateswarlu, Ch., Reddy, A. D., Nonlinear model predictive control of reactive distillationbased on stochastic optimization. Industrial and Engineering Chemistry Research, 2008,47(18):6949-6960.
[50] Boettcher, S., Percus, A. G., Extremal optimization: methods derived from co-evolution.ProceedingsoftheGeneticandEvolutionaryComputationConference,1999,825-832.
[51] Bak, P., Tang, C., Wiesenfeld, K., Self-organized criticality: an explanation of 1/f noise.PhysicalReviewLetter,1987,59(4):381-384.
[52]Bak, P., How nature works: the science of self-organised criticality. Berlin: Springer-Verlag,1996.
[53] Malamud, B. D., Morein, G., Turcotte, D. L., Forest fires: an example of self-organizedcriticalbehavior.Science, 1998,281(5384):1840-1842.
[54] Smethurst, D. P., Williams, H. C., Are hospital waiting lists self-regulating? Nature, 2001,410:652-653.
[55] Bhattacharya, K., Manna, S. S., Self-organized critical models of earthquakes. Physica A:StatisticalMechanicsanditsApplications,2007,384(1):15-20.
[56]Chen,Y.,Zhou,Y.,Scalinglawsandindicationsofself-organizedcriticalityinurbansystems.Chaos,SolitonsandFractals,2008, 35:85–98.
[57] Bak, P., Sneppen, K., Punctuated equilibrium and criticality in a simple model of evolution.PhysicalReviewLetter,1993,71(24):4083-4086.
[58]陈泯融,基于极值动力学的优化方法及其应用研究.博士学位论文,上海交通大学.2008.
[59]Boettcher,S.,Percus,A. G.,Extremaloptimizationfor graph partitioning.PhysicalReviewE,2001,64(2):1-13.
[60]Menai, M. E., Batouche, M.,Efficient initial solution toextremal optimization algorithm forweightedMAXSATproblem.LectureNotesinComputerScience, 2003,2718:319-338.
[61] Boettcher, S., Percus, A. G., Extremal optimization at the phase transition of thethree-coloring problem.PhysicalReviewE,2004,69(6):1-8.
[62] Chen, Y. W., Lu, Y. Z., Chen, P., Optimization with extremal dynamics for the travelingsalesmanproblem.PhysicaA:StatisticalMechanicsanditsApplications,2007,1(1):115-123.
[63]Chen, M. R., Lu, Y. Z., Anovel elitist multiobjective optimization algorithm: multiobjectiveextremaloptimization.EuropeanJournalofOperationalResearch,2008,188(3):637-651.
[64] Chen, P., Lu, Y. Z., Chen, Y. W., Extremal optimization combined with LM gradient searchfor MLP network learning. International Journal of Computational Intelligence Systems, 2010,3(5):622-631.
[65]Chen, P., Lu, Y. Z., Nonlinear modelpredictive control with the integration ofsupport vectormachine andextremaloptimization.Prodceedingofthe8thWorld Congresson IntelligentControlandAutomation,2010,3167-3172.
[66]Dawkins,R.,TheSelfishGene.Oxford:OxfordUniversityPress,1976.
[67]Moscato,P.,On evolution,search, optimization,genetic algorithms, and martial arts:towardsmemetic algorithms. California Institute of Technology, Pasadena, 1989, Caltech ConcurrentComputationProgram,Report.826.
[68] Cotta, C., Moscato, P., Memetic algorithms. Handbook of Approximation Algorithms andMetaheuristics. NewYork:CRCPress,2007.
[69]Wolpert,D.H.,Macready,W.G., Nofreelunchtheoremsforoptimization.IEEETransactionsonEvolutionaryComputation,1997,1(1):67-82.
[70]段海滨,张祥银,徐春芳,仿生智能计算.北京:科学出版社,2011.
[71] Digalakis, J., Margaritis, K., Performance comparison of memetic algorithms. AppliedMathematicsandComputation,2004,158(1):237-252.
[72]Ozcan, E., Memes, self-generation and nurse rostering. Lecture Notes in Computer Science,2007,3867:85-104.
[73]Lü, Z., Hao, J. K., Amemetic algorithm for graph coloring. European Journal ofOperationalResearch,2010,203(1):241-250.
[74] Nagata, Y., Braysy, O., Dullaert, W., A penalty-based edge assembly memetic algorithm forthe vehicle routing problem with time windows. Computers & Operations Research,2010, 37(4):724-737.
[75]Krasnogor, N.,Gustafson, S., Astudyon the use of“self-generation”in memetic algorithms.NaturalComputing,2004,3(1):53-76.
[76] Zhu, Z., Ong, Y. S., Zurada, J. M., Identification of full and partial class relevant genes.IEEE/ACMTransactionsonComputationalBiologyandBioinformatics,2010,7(2):263-277.
[77] Pacsuski, M., Maslov, S., Bak, P., Avalanche dynamics in evolution, growth, and depiningmodels.PhysicalReviewE, 1996,53(1):414-443.
[78] Sneppen, K., Extremal dynamics and punctuated co-evolution. Physica A: Statistical andTheoreticalPhysics,1995,221(1-3):168-179.
[79]杨根科,陈玉旺,戴博,系统工程中自组织优化方法及其应用.系统工程理论与实践,2008,(6):142-147.
[80]刘静,协同进化算法及其应用研究.博士学位论文,西安电子科技大学.2004.
[81] Ehrlich, P. R., Raven, P. H., Butterflies and plants: a study in coevolution. Society for theStudyofEvolution.1964,18(4):586-608.
[82]Haken,H.,Synergetics.Berlin:Springer, 1977.
[83]Jazen,D.H.,Whenisit coevolution.Evolution, 1980,34(3):611-612.
[84]李碧,协同进化算法的研究及其应用.博士学位论文,华南理工大学,2010.
[85] Smith, J. E., Coevolving memetic algorithms: a review and progress report. IEEETransactionsonSystems,Man,andCybernetics,PartB:Cybernetics,2007,37(1):6-17.
[86]梅可玉,论自组织临界性与复杂系统的演化行为.自然辩证法研究,2004,20(7):6-9.
[87] Levenberg, K., A method for the solution of certain nonlinear problems in least squares.QuarterlyofAppliedMathematics.1944,2:164-166.
[88]Marquardt, D.W.,An algorithm for least-squares estimation of nonlinear inequalities. SIAMJournalonAppliedMathematics.1963,11(2):431-441.
[89]Nocedal,J.,Wright,S.J.,Numericaloptimization.NewYork:Springer, 1999.
[90]Hagan, M. T., Menhaj, M. B., Training feedforward networks with the Marquardt algorithm.IEEETransactionsonNeuralNetworks,1994,5(6):989-993.
[91]Sousa, F. L., Vlassov, V., Ramos, F. M., Generalized extremal optimization: an application inheatpipedesign.AppliedMathematicalModelling,2004,28(10):911-931.
[92]Haykin,S., NeuralNetworks:AComprehensiveFoundation.NewYork:Macmillan, 1994.
[93] Werbos, P., Beyond Regression: New Tools for Prediction and Analysis in the BehavioralSciences.Ph.D.Thesis,HarvardUniversity,1974.
[94] Rumelhart, D. E., Hinton, G. E., Williams, R. J., Learning representations by backpropagatingerrors.Nature,1986a,323(9):533-536.
[95]Rumelhart, D. E., Hinton, G. E., Williams, R. J., Learning InternalRepresentations by ErrorPropagation.MA:MITPress,1986b,318-362.
[96]Salomon, R.,Evolutionaryalgorithms and gradient search: similarities and differences. IEEETransactionsonEvolutionaryComputation,1998,2(2):45-55.
[97] Jacobs, R. A., Increased rates of convergence through learning rate adaptation. NeuralNetworks, 1988,1(4):295-307.
[98]Rigler, A. K., Irvine, J. M., Vogl, T. P., Rescaling of variables in back propagation learning.NeuralNetworks,1991,4(2):225-229.
[99]Fukuoka,Y.,Matsuki,H.,Minamitani,H., Ishida,A.,Amodifiedbackpropagationmethodtoavoidfalselocalminima.NeuralNetworks, 1998,11(6):1059-1072.
[100] Hush, D. R., Training a sigmoidal node is hard. Neural Computation, 1999,11(5):1249-1260.
[101] Engelbrecht, A. P., Computational intelligence: an introduction. New York: John Wiley &Sons,2007.
[102] Arifovic, J., Gencay, R., Using genetic algorithms to select architecture of a feedforwardartificial neural network. Physica A: Statistical Mechanics and its Applications, 2001, 289(3-4):574-594.
[103] Li, S. J., Li, Y., Liu, Y., Xu, Y. F., A GA-based NN approach for makespan estimation.AppliedMathematicsand Computation, 2007a,185(2):1003-1014.
[104]Yao, X., Islam, Md. M.,Evolving artificial neural network ensembles. IEEE ComputationalIntelligenceMagazine,2008,3(1):31-42.
[105] Dengiz, B., Alabas-Uslu, C., Dengiz, O.,Atabu search algorithm for the training of neuralNetworks.JournaloftheOperationalResearchSociety,2009,60(2):282-291.
[106] Fasih, A., Chedjou, C. J., Kyamakya, K.,Cellular neural networks-based genetic algorithmfor optimizing the behavior of an unstructured robot. International Journal of ComputationalIntelligenceSystems,2009,124-133.
[107] Reyaz-Ahmed, A., Zhang Y. Q., Harrison, R. W., Granular decision tree and evolutionaryneural SVM for protein secondary structure prediction. International Journal of ComputationalIntelligenceSystems,2009,2(4):343-352.
[108] Ackley, D. H., A connectionist machine forgenetic hill climbing. Boston: Kluwer Boston,1987.
[109] Martinsen, F., Biegler, L. T., Foss, B. A., Anew optimization algorithm with application tononlinearMPC.JournalofProcessControl,2004,14(8):853-865.
[110]Wang, Y., Cai, Z., Zhou, Y., Fan, Z., Constrained optimization based on hybrid evolutionaryalgorithm and adaptive constraint-handling technique. Structural and MultidisciplinaryOptimization,2009,37:395-413.
[111]Handoko,S.D.,Kwoh,C.K.,Ong,Y. S.,Lim,M.H.,Astudyon constrainedMAusingGAand SQP: analytical vs. finite-differencegradients. Proceedings of the Congress on EvolutionaryComputation,2008,4031-4038.
[112] Vargas, M. C., Torres-Jimenez, J., Ruiz-Suarez, J. C., Analysis of X-ray diffraction datausing a hybrid stochastic optimization method. Journal of Physics A: Mathematical and General,2002,35:3865-3876.
[113] Runarsson, T. P., Yao, X., Stochastic ranking for constrained evolutionary optimization.IEEETransactionsonEvolutionaryComputation,2000,4:284-294.
[114]Zhang, M., Luo, W., Wang, X., Differential evolution with dynamic stochastic selection forconstrainedoptimization.InformationSciences, 2008,178:3043-3074.
[115] Wilson, R. B., ASimplicial Algorithm for Concave Programming. Ph. D. Thesis, HarvardUniversity,1963.
[116] Han, S. P., Superlinearly convergent variable metric algorithms for general nonlinearprogrammingproblems.MathematicalProgramming,1976,11:263-282.
[117] Powell, M. J. D., A fast algorithm for nonlinearly constrained optimization calculations.LectureNotesinMathematics, 1978,630:144-157.
[118] Moscato, P., Cotta, C., A gentle introduction to memetic algorithms. Handbook ofMetaheuristics.Boston: KluwerAcademicPublishers, 2003,105-143.
[119] Martin, O. C., Monasson, R., Zecchina, R., Statistical mechanics methods and phasetransitionsinoptimizationproblems.TheoreticalComputerScience, 2001,265(1-2):3-67.
[120] Murty, K. G., Kabadi, S. N., Some NP complete problems in quadratic and nonlinearprogramming,MathematicalProgramming,1987, 39:117-129.
[121]Fukumizu, K., Amari, S.,Local minima and plateausin hierarchicalstructures ofmultilayerperceptrons.NeuralNetworks,2000,13(3):317-327.
[122] Mezura-Montes, E., Carlos, A. C. C., Asimple multimemberedevolution strategy to solveconstrained optimization problems. IEEE Transactions on Evolutionary Computation, 2005,9:1-17.
[123] Zhu, W., Ali, M. M., Solving nonlinearly constrained global optimization problem via anauxiliary function method. Journal of Computational and Applied Mathematics, 2009,230:491-503.
[124]席裕庚.预测控制.北京:国防工业出版社.1993.
[125] Na, M.G., Upadhyaya, B.R., Model Predictive Control of an SP-100 Space Reactor UsingSupportVectorRegressionandGeneticOptimization.IEEETransactionsonNuclearScience.2006,53(4):2318-2327.
[126]Ong,C.J.,Sui,D.,Gilbert,E.G.,Enlargingtheterminalregionofnonlinearmodelpredictivecontrolusingthesupportvectormachinemethod.Automatica.2006,42(6):1011-1016.
[127] Xi, X.C., Poo, A.N., Chou, S.K., Support vector regression model predictive control on aHVACplant.ControlEngineeringPractice.2007,15(8):897-908.
[128] Li, L.J., Su, H.Y., Chu, J., Generalized Predictive Control with Online Least SquaresSupportVectorMachines.ActaAutomaticaSinica.2007b,33(11):1182-1188.
[129]Zhang,R.D.,Wang,S.Q., Supportvectormachinebasedpredictivefunctionalcontroldesignforoutputtemperatureofcokingfurnace.JournalofProcessControl.2008,18(5):439-448.
[130] Forssell, N., Ljung, L., Closed-loop identification revisited. Automatica, 1999,35(7):1215-1241.
[131] Steve, G., Support vector machines classification and regression. Image, Speech, andIntelligentSystemsGroup,UniversityofSouthampton,UK,1998,ISISTechnicalReport.
[132] Min, J. H., Lee, Y. C., Bankruptcy prediction using support vector machine with optimalchoiceofkernelfunctionparameters.ExpertSystemswithApplications, 2005,28(4):603-614.
[133] Ali, S., Smith, K. A., A meta-learning approach to automatic kernel selection for supportvectormachines.Neurocomputing, 2006,70(1-3):173-186.
[134] Jeng, J. T., Hybrid approach of selecting hyperparameters of support vector machine forregression. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2006,36(3):699-709.
[135] Hou, S. M., Li, Y. R., Short-term fault prediction based on support vector machines withparameter optimization by evolution strategy. Expert Systems with Applications, 2009,36(10):12383-12391.
[136]Lorena, A.C., De Carvalho, A.,Evolutionarytuning of SVM parameter values in multiclassproblems.Neurocomputing,2008,71(16-18):3326-3334.
[137]Zhang,X.L.,Chen,X. F.,He,Z.J.,AnACO-basedalgorithmforparameteroptimizationofsupportvectormachines.ExpertSystemswithApplications,2010,37(9):6618-6628.
[138] Hsu, C. W., Chang, C. C., Lin, C. J., A practical guide to support vector classification.NationalTaiwanUniversity,Taiwan,2004, Report.
[139] Friedrichs, F., Igel, C., Evolutionary tuning of multiple SVM parameters. Neurocomputing,2005,64:107-117.
[140]Avci, E., Selectingofthe optimalfeature subsetand kernelparametersin digitalmodulationclassification by using hybrid genetic algorithm-support vector machines: HGASVM. ExpertSystemswithApplications, 2009,36(2):1391-1402.
[141] Pai, P. F., Hong, W. C., Support vector machines with simulated annealing algorithms inelectricityloadforecasting.EnergyConversionandManagement,2005,46(17):2669-2688.
[142] Mao, Y., Zhou, X., Pi, D., Sun, Y., Wong, S. T.C., Parameters selection in gene selectionusing Gaussian kernel support vector machines by genetic algorithm, Journal of ZhejiangUniversity-ScienceB,2005,6(10):961-973.
[143] Wu, C. H., Tzeng, G. H., Goo, Y. J., Fang, W. C., A real-valued genetic algorithm tooptimizetheparametersofsupportvectormachineforpredictingbankruptcy.ExpertSystemswithApplications, 2007,32(2):397-408.
[144] Tang, X., Zhuang, L., Jiang, C., Prediction of silicon content in hot metal using supportvector regression based on chaos particle swarm optimization. Expert Systems with Applications,2009,36(9):11853-11857.
[145] Saini, L. M., Aggarwal, S. K., Kumar, A., Parameter optimization using genetic algorithmfor support vector machine-based price-forecasting model in National electricity market. IETGeneration,Transmission&Distribution,2010,4(1):36-49.
[146]Wu, Q., Ahybrid-forecasting model based on Gaussian support vector machine and chaoticparticleswarmoptimization.ExpertSystemswithApplications,2010,37(3):2388-2394.
[147] Howley, T., Madden, M. G., The genetic kernel support vector machine: description andevaluation.ArtificialIntelligenceReview,2005,24(3-4):379-395.
[148] Thadani, K., Jayaraman, A., Sundararajan, V., Evolutionary selection of kernels in supportvector machines. International Conference on Advanced Computing and Communications, 2006,19-24.
[149] Wu, C. H., Tzeng, G. H., Lin, R. H., A novel hybrid genetic algorithm for kernel functionand parameteroptimization in supportvectorregression. ExpertSystemswith Applications,2009,36(3):4725-4735.
[150]Chen,P., Lu, Y. Z., Extremal optimization for optimizing kernel function and its parametersinsupportvectorregression.JournalofZhejiangUniversity-SCIENCEC,2011,12(4):297-306.
[151]Chuang,C.C.,Lee,Z. J.,Hybridrobustsupportvectormachinesforregressionwithoutliers.AppliedSoftComputing, 2011,11(1):64-72.
[152] Qiao, J. F., Wang, H. D., A self-organizing fuzzy neural network and its applications tofunctionapproximationandforecastmodeling.Neurocomputing,2008,71(4-6):564-569.
[153] Narendra, K. S., Parthasarathy, K., Identification and control of dynamical systems usingneuralnetworks.IEEETransactionsonNeuralNetworks, 1990,1(1):4-27.
[154] Pearson, R. K., Nonlinear empirical modeling techniques. Computers and ChemicalEngineering,2006,30:1514-1528.
[155] Matsuura, T., Kato, M., Concentration stability of the isothermal reactor. ChemicalEngineeringScience,1967,22(2):171-183.
[156] Li, W. C., Biegler, L. T., Process control strategies for constrained nonlinear systems.IndustrialandEngineeringChemistryResearch,1988,27(8):1421-1433.
[157] Chen, P., Lu, Y. Z., Memetic algorithm-based neural network learning for basic oxygenfurnaceendpointprediction.JournalofZhejiangUniversity-SCIENCEA,2010,11(11):841-848.
[158] Cox, I. J., Lewis, R. W., Ransing, R.S., Laszczewski, H., Berni, G., Application of neuralcomputing in basic oxygen steelmaking. Journal of Materials Processing Technology, 2002,120(1-3):310-315.
[159] Wang, X., Wang, Z. J., Tao, J., Multiple neural network modeling method for carbon andtemperature estimation in basic oxygen furnace. Lecture Notes in Computer Science, 2006,3973:870-875.
[160] Tao, J., Wang, X., Chai, T. Y., Intelligent control method and application for BOFsteelmaking.ProceedingsoftheIFACWorldCongress,2002,1071-1076.
[161]Fileti, A. M. F., Pacianotto, T. A., Cunha, A. P., Neuralmodelling helps the BOSprocess toachieve aimed end-point conditions in liquid steel. Engineering Applications of ArtificialIntelligence,2006,19(1):9-17.
[162] Feng, M. X., Li, Q., Zou, Z. S., An outlier identification and judgment method for animproved neural-network BOF forecasting model. Steel Research International, 2008,79(5):323-332.
[163]Pendharkar, P. C., Genetic algorithm based neural network approaches for predicting churnincellularwirelessnetworkservices.ExpertSystemswithApplications,2009,36(3):6714-6720.
[164] Sedki, A., Ouazar, D., EI Mazoudi, E., Evolving neural network using real coded geneticalgorithm for daily rainfall-runoff forecasting. Expert Systems with Applications, 2009,36(3):4523-4527.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |