Automatic design of fuzzy logic controller using a genetic algorithm—to predict power requirement and surface finish in grinding

Abstract We have developed a method for automatic design of fuzzy logic controller (FLC) using a genetic algorithm (GA). The performance of an FLC depends on its knowledge base (KB), which consists of membership function distributions (also known as data base) and rule base. To design a proper KB of the FLC, the designer should have a thorough knowledge of the process to be controlled. Sometimes, it becomes difficult to gather knowledge of the process beforehand. Thus, designing the proper KB of an FLC is not an easy task. In this paper, a new approach for designing the KB of an FLC (using a GA) is proposed and its effectiveness is compared to a previous approach based on GA-fuzzy combination for making predictions of power requirement and surface finish in grinding (a machining process to obtain smooth surface on the work).

[1]  Dilip Kumar Pratihar,et al.  Design of a genetic-fuzzy system to predict surface finish and power requirement in grinding , 2004, Fuzzy Sets Syst..

[2]  T. Warren Liao,et al.  A neural network approach for grinding processes: Modelling and optimization , 1994 .

[3]  Ichiro Inasaki,et al.  Modelling and Simulation of Grinding Processes , 1992 .

[4]  H. Ishigami,et al.  Structure optimization of fuzzy neural network by genetic algorithm , 1995 .

[5]  Y. Hasegawa,et al.  Reinforcement learning method for generating fuzzy controller , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[6]  Isao Hayashi,et al.  A learning method of fuzzy inference rules by descent method , 1992 .

[7]  John Y. Cheung,et al.  Design of a fuzzy controller using input and output mapping factors , 1991, IEEE Trans. Syst. Man Cybern..

[8]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[9]  Yoshio Ichida,et al.  Grinding mode identification and surface quality prediction using neural network in grinding of silicon nitride , 1996 .

[10]  Isao Hayashi,et al.  NN-driven fuzzy reasoning , 1991, Int. J. Approx. Reason..

[11]  Ming-Kuen Chen,et al.  Neural network modelling and multiobjective optimization of creep feed grinding of superalloys , 1992 .

[12]  Philip R. Thrift,et al.  Fuzzy Logic Synthesis with Genetic Algorithms , 1991, ICGA.

[13]  G. J. Trmal,et al.  Optimum selection of grinding parameters using process modelling and knowledge based system approach , 1991 .

[14]  Bart Kosko,et al.  Neural networks and fuzzy systems , 1998 .

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

[16]  Li Yan,et al.  Applications of artificial intelligence in grinding , 1994 .

[17]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[18]  L. Darrell Whitley,et al.  Genetic algorithms and neural networks: optimizing connections and connectivity , 1990, Parallel Comput..

[19]  Frank Klawonn,et al.  Combining Neural Networks and Fuzzy Controllers , 1993, FLAI.

[20]  T. Furuhashi,et al.  A study on fuzzy rules discovery using Pseudo-Bacterial Genetic Algorithm with adaptive operator , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[21]  G. J. Trmal,et al.  A dynamic modelling approach to computer aided optimum selection of grinding parameters , 1993 .

[22]  T. Hashiyama,et al.  A study on finding fuzzy rules for semi-active suspension controllers with genetic algorithm , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[23]  Jyh-Shing Roger Jang,et al.  Self-learning fuzzy controllers based on temporal backpropagation , 1992, IEEE Trans. Neural Networks.

[24]  Francisco Herrera,et al.  GENERATING FUZZY RULES FROM EXAMPLES USING GENETIC ALGORITHMS , 1995 .

[25]  Hugh M. Cartwright,et al.  Applications of artificial intelligence in chemistry , 1993 .

[26]  M. Sugeno,et al.  Derivation of Fuzzy Control Rules from Human Operator's Control Actions , 1983 .

[27]  Ichiro Inasaki,et al.  Intelligent Data Base for Grinding Operations , 1993 .

[28]  R. M. Goodman,et al.  Learning fuzzy rule-based neural networks for function approximation , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[29]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[30]  Chia-Ju Wu,et al.  Design of fuzzy logic controllers using genetic algorithms , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).