Neural fuzzy inference systems with knowledge-based cultural differential evolution for nonlinear system control

Abstract This study proposes a knowledge-based cultural differential evolution (KCDE) method for neural fuzzy inference systems (NFIS). Cultural algorithms acquire the belief space from the evolving population space and then exploit that information to guide the search. The proposed KCDE method adopts the mutation strategies of differential evolution as knowledge sources to influence the population space. The proposed KCDE method uses these knowledge sources, including normative knowledge, situational knowledge, domain knowledge, history knowledge, and topographic knowledge, to optimize the parameters of the NFIS model to avoid falling in a local optimal solution and to ensure the searching capacity of approximate global optimal solution. Experimental results demonstrate that the proposed NFIS-KCDE method performs well in nonlinear system control problems.

[1]  Chi-Hsu Wang,et al.  Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN) , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[2]  B. Widrow,et al.  The truck backer-upper: an example of self-learning in neural networks , 1989, International 1989 Joint Conference on Neural Networks.

[3]  Sushmita Mitra,et al.  Neuro-fuzzy rule generation: survey in soft computing framework , 2000, IEEE Trans. Neural Networks Learn. Syst..

[4]  Clive D. L. Wynne,et al.  Animal Cognition: The Mental Lives of Animals , 2002 .

[5]  K.P. Wong,et al.  Application of Differential Evolution Algorithm for Transient Stability Constrained Optimal Power Flow , 2008, IEEE Transactions on Power Systems.

[6]  R. Reynolds.  AN INTRODUCTION TO CULTURAL ALGORITHMS , 2008 .

[7]  P. Kokotovic,et al.  Nonlinear control via approximate input-output linearization: the ball and beam example , 1992 .

[8]  Carlos A. Coello Coello,et al.  Evolutionary multiobjective optimization using a cultural algorithm , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[9]  Jouni Lampinen,et al.  A Trigonometric Mutation Operation to Differential Evolution , 2003, J. Glob. Optim..

[10]  Chin-Teng Lin,et al.  An online self-constructing neural fuzzy inference network and its applications , 1998, IEEE Trans. Fuzzy Syst..

[11]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[12]  Wei-Yen Wang,et al.  An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Takanori Shibata,et al.  Genetic Algorithms And Fuzzy Logic Systems Soft Computing Perspectives , 1997 .

[14]  Chin-Teng Lin,et al.  An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications , 1998 .

[15]  Plamen Angelov,et al.  Evolving Rule-Based Models: A Tool For Design Of Flexible Adaptive Systems , 2002 .

[16]  Robert G. Reynolds,et al.  The role of culture in the emergence of decision-making roles: An example using cultural algorithms , 2008 .

[17]  Rainer Storn,et al.  System design by constraint adaptation and differential evolution , 1999, IEEE Trans. Evol. Comput..

[18]  Robert G. Reynolds,et al.  Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[19]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[20]  D.P. Filev,et al.  An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[22]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[23]  Chin-Teng Lin,et al.  A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control , 2008, IEEE Transactions on Fuzzy Systems.

[24]  H. Ishibuchi.  Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases , 2004 .

[25]  Cheng Jian Lin,et al.  Nonlinear System Control Using Compensatory Neuro-Fuzzy Networks(Optimization and Control)( Nonlinear Theory and its Applications) , 2003 .

[26]  Shigeo Abe,et al.  Neural Networks and Fuzzy Systems , 1996, Springer US.

[27]  Tarun Khanna,et al.  Foundations of neural networks , 1990 .

[28]  Xin Yao,et al.  Differential evolution for high-dimensional function optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[29]  Chun-Fei Hsu,et al.  Self-Organizing Adaptive Fuzzy Neural Control for a Class of Nonlinear Systems , 2007, IEEE Transactions on Neural Networks.

[30]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[31]  Robert G. Reynolds,et al.  Cultural algorithms in dynamic environments , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[32]  Chin-Teng Lin,et al.  A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[33]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[34]  Derrick H. Nguyen,et al.  Neural networks for self-learning control systems , 1990 .

[35]  Sigeru Omatu,et al.  Process control by on-line trained neural controllers , 1992, IEEE Trans. Ind. Electron..

[36]  Ganapati Panda,et al.  Identification of nonlinear dynamic systems using functional link artificial neural networks , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[37]  Cecilia Heyes,et al.  Evolution of Cognition , 2001 .