Title :
Reasoning and learning method for fuzzy rules using neural networks with adaptive structured genetic algorithm
Author :
Ichimura, Takumi ; Takano, Takeshi ; Tazaki, Eiichiro
Author_Institution :
Dept. of Control & Syst. Eng., Toin Univ. of Yokohama, Japan
Abstract :
In this paper, we present a reasoning and learning method for fuzzy rules using neural networks with adaptive structured genetic algorithm. This adaptive structured genetic algorithm can determine the network structure and their weights solely by an evolutionary process. With this approach, no a priori assumptions about topology are needed and the only information required is the input and output characteristics of the task. The adaptive structured genetic algorithm can generate or annihilate the specified units respectively in hidden layer to achieve an overall good system, without using back propagation or any other learning algorithm
Keywords :
fuzzy logic; fuzzy neural nets; genetic algorithms; learning (artificial intelligence); adaptive structured genetic algorithm; fuzzy rules; learning method; neural networks; reasoning; Adaptive control; Adaptive systems; Fuzzy control; Fuzzy neural networks; Fuzzy reasoning; Genetic algorithms; Learning systems; Neural networks; Neurons; Programmable control;
Conference_Titel :
Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-2559-1
DOI :
10.1109/ICSMC.1995.538289
