Development of if-then rules with the use of DNA coding

Fuzzy logic has been widely used in industry. Knowledge acquisition has been one of the most important problems of fuzzy logic. Experts often have difficulties in describe their know hows. Acquisition of fuzzy if-then rules from experts’ operating data by neural networks and fuzzy neural networks have been vigorously studied. In case those operators are not available, knowledge acquisition is not possible. A new technology to discover knowledge is required. Genetic algorithms (GAs) [1, 2] have been widely studied and applied to many problems.

[1]  John H. Holland,et al.  COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS1 , 1978 .

[2]  Donald A. Waterman,et al.  Pattern-Directed Inference Systems , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  D. E. Goldberg,et al.  Genetic Algorithm in Search , 1989 .

[4]  Charles L. Karr,et al.  Improved Fuzzy Process Control of Spacecraft Autonomous Rendezvous Using a Genetic Algorithm , 1990, Other Conferences.

[5]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[6]  C. L. Karr Design of an Adaptive Fuzzy Logic Controller Using a Genetic Algorithm , 1991, ICGA.

[7]  Manuel Valenzuela-Rendón,et al.  The Fuzzy Classifier System: A Classifier System for Continuously Varying Variables , 1991, ICGA.

[8]  Charles L. Karr,et al.  Design of a cart-pole balancing fuzzy logic controller using a genetic algorithm , 1991, Defense, Security, and Sensing.

[9]  Willfried Wienholt,et al.  A Refined Genetic Algorithm for Parameter Optimization Problems , 1993, ICGA.

[10]  Controlling Excessive Fuzziness in a Fuzzy Classifier System , 1993, ICGA.

[11]  Hideyuki Takagi,et al.  Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques , 1993, ICGA.

[12]  Yoshiki Uchikawa,et al.  A study on apportionment of credits of fuzzy classifier system for knowledge acquisition of large scale systems , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[13]  Y. Uchikawa,et al.  A New Approach to Genetic Based Machine Learning and an Efficient Finding of Fuzzy Rules - Proposal of Nagoya Approach - , 1994, IEEE/Nagoya-University World Wisepersons Workshop.

[14]  Takeshi Furuhashi,et al.  An Acquuitsition of Control Knowledge Using Multiple Fuzzy Classifier Systems , 1994 .

[15]  Andrea Bonarini,et al.  Evolutionary learning of general fuzzy rules with biased evaluation functions: competition and cooperation , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[16]  Yoshiki Uchikawa,et al.  Acquisition of Fuzzy Rules from DNA Coding Method , 1995, IEEE/Nagoya-University World Wisepersons Workshop.

[17]  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.

[18]  T. Furuhashi,et al.  DNA coding method and a mechanism of development for acquisition of fuzzy control rules , 1996, Proceedings of IEEE 5th International Fuzzy Systems.