Paper Info
Title
A new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules
Abstract
In this paper, we develop a new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules by using training input-output data, based on the gradient descent method. A major advantage of this approach is that fuzzy rules or membership functions can be learned without changing the form of fuzzy rule table used in usual fuzzy applications, so that the case of non-firing or weak-firing can be well avoided, which is different from the conventional neuro-fuzzy learning algorithms. Moreover, some properties of the developed approach are also discussed. Finally, the efficiency of the developed approach is illustrated by means of identifying non-linear functions. (C) 2000 Elsevier Science B.V. All rights reserved.
Year
DOI
Venue
2000
10.1016/S0165-0114(98)00238-3
Fuzzy Sets and Systems
Field
DocType
Volume
Neuro-fuzzy,Fuzzy classification,Defuzzification,Fuzzy set operations,Algorithm,Artificial intelligence,Fuzzy associative matrix,Fuzzy number,Membership function,Machine learning,Mathematics,Fuzzy rule
Journal
112
Issue
ISSN
Citations 
1
0165-0114
20
PageRank 
References 
Authors
1.34
7
2
Name
Order
Citations
PageRank
Yan Shi128527.64
Masaharu Mizumoto2766406.85