DocumentCode
3254422
Title
Applying adaptive structured genetic algorithm to reasoning and learning method for fuzzy rules using neural networks
Author
Ichimura, Takumi ; Tazaki, Eiichiro
Author_Institution
Dept. of Control & Syst. Eng., Toin Univ., Yokohama, Japan
Volume
6
fYear
1995
fDate
Nov/Dec 1995
Firstpage
3124
Abstract
In this paper, we present a reasoning and learning method for fuzzy rules using neural networks with an adaptive structured genetic algorithm. This adaptive structured genetic algorithm is to determine the neural network structures and their input weights by an evolutionary process. Without using general learning algorithm in neural networks, the adaptive structured genetic algorithm can generate or annihilate the specified units respectively in hidden layer to achieve an overall good system
Keywords
fuzzy neural nets; genetic algorithms; inference mechanisms; learning (artificial intelligence); adaptive structured genetic algorithm; evolutionary process; fuzzy rules; learning; neural networks; reasoning; Algorithm design and analysis; Fuzzy control; Fuzzy neural networks; Fuzzy reasoning; Fuzzy systems; Genetic algorithms; Learning systems; Neural networks; Neurons; Programmable control;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location
Perth, WA
Print_ISBN
0-7803-2768-3
Type
conf
DOI
10.1109/ICNN.1995.487283
Filename
487283
Link To Document