Intelligent Learning Of Fuzzy Logic Controllers Via Neural Network And Genetic Algorithm

Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy setsand proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper develops methodologies to learn and optimize fuzzy logic controller parameters based on neural network and genetic algorithm. The strategies developed have been applied to control an inverted pendulum and results have been compared for three different fuzzy logic controllers developed with the help of iterative learning from operator experience, genetic algorithm and neural network. The results show that GeneticFuzzy and Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.

[1]  Guido Bologna,et al.  FDIMLP: A new neuro-fuzzy model , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[2]  Rudolf Kruse,et al.  Neuro-fuzzy control based on the NEFCON-model: recent developments , 1999, Soft Comput..

[3]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[4]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[6]  E. Mizutani,et al.  Levenberg-Marquardt method for ANFIS learning , 1996, Proceedings of North American Fuzzy Information Processing.

[7]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[8]  D.M. Auslander,et al.  Real time neuro-fuzzy control of a nonlinear dynamic system , 1996, Proceedings of North American Fuzzy Information Processing.

[9]  Germano Lambert-Torres,et al.  A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems , 1998, IEEE Trans. Neural Networks.

[10]  Abdellatif Rahmoun,et al.  A genetic-based neuro-fuzzy generator: NEFGEN , 2001, Proceedings ACS/IEEE International Conference on Computer Systems and Applications.

[11]  D. E. Goldberg,et al.  Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[12]  Raúl Hector Gallard,et al.  Genetic algorithms + Data structure = Evolution programs , Zbigniew Michalewicz , 1999 .

[13]  Devendra P. Garg,et al.  Genetic Algorithm Based PD Control and Fuzzy Logic Control of a Two Link Robot , 2002 .

[14]  Ronald R. Yager,et al.  Fuzzy sets, neural networks, and soft computing , 1994 .

[15]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[16]  Clarence W. de Silva,et al.  Intelligent machines: myths and realities , 2000 .

[17]  Marzuki Khalid,et al.  Tuning of a neuro-fuzzy controller by genetic algorithm , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[18]  George N. Saridis,et al.  L-Q design of PID controllers for robot arms , 1985, IEEE J. Robotics Autom..

[19]  M. Niestroy.  Optimal controller approximation using neural and fuzzy-neural networks , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[20]  N. K. Bose,et al.  Neural Network Fundamentals with Graphs, Algorithms and Applications , 1995 .

[21]  G. M. Varazi,et al.  Constructive algorithm for neuro-fuzzy networks , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[22]  Paolo Rocco,et al.  Stability of PID control for industrial robot arms , 1996, IEEE Trans. Robotics Autom..