Paper Info
Title
Acquisition Of Fuzzy Rules Using Fuzzy Neural Networks With Forgetting
Abstract
We acquire fuzzy rules from data using a fuzzy neural network. First, we generate an initial fuzzy neural network of the specified number of fuzzy rules that have the less number of good membership functions generated using a self-organization algorithm by T. Kohonen. Then, we tune and prune fuzzy rules based on a structural leaning algorithm with forgetting by M. Ishikawa, where the numerals in the consequent part and the center values and widths of membership functions in the antecedent part are tuned and forgotten a little, and thus redundant rules and variables are pruned to acquire simpler, general rules. We apply the method to the iris classification problem by R.A. Fisher and have a very good result.
Year
DOI
Venue
1997
10.1109/ICNN.1997.614436
1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4
Keywords
Field
DocType
neural networks,data engineering,mathematics,membership function,training data,fuzzy systems,fuzzy sets,fuzzy set theory,learning artificial intelligence,art,self organization,forgetting,fuzzy neural network
Neuro-fuzzy,Fuzzy classification,Defuzzification,Fuzzy set operations,Artificial intelligence,Fuzzy associative matrix,Fuzzy number,Type-2 fuzzy sets and systems,Membership function,Mathematics
Conference
Citations 
PageRank 
References 
2
0.99
2
Authors
4
Name
Order
Citations
PageRank
Motohide Umano118328.91
shiro fukunaka220.99
Itsuo Hatono313318.38
Hiroyuki Tamura417630.79