Paper Info
Title
An improvement of neuro-fuzzy learning algorithm for tuning fuzzy rules
Abstract
Based on the fuzzy clustering method, we improved a neuro-fuzzy learning algorithm. In this improved approach, before learning fuzzy rules we extract typical data from training data by using fuzzy c-means clustering algorithm, in order to remove redundant data and resolve conflicts in data, and make them as practical training data. By these typical data, fuzzy rules can be tuned by using the neuro-fuzzy learning algorithm. Therefore, the learning time can be expected to be reduced and the fuzzy rules generated by the improved approach are reasonable and suitable for the identified system model. Moreover, the efficiency of the improved method is also shown by identifying nonlinear functions.
Year
DOI
Venue
2001
10.1016/S0165-0114(98)00440-0
Fuzzy Sets and Systems
Keywords
Field
DocType
Fuzzy rule generation,Neuro-fuzzy learning algorithm,Fuzzy c-means clustering algorithm
Fuzzy clustering,Neuro-fuzzy,Fuzzy classification,Defuzzification,Fuzzy set operations,Algorithm,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy associative matrix,Fuzzy number,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
118
2
0165-0114
Citations 
PageRank 
References 
17
1.01
8
Authors
2
Name
Order
Citations
PageRank
Yan Shi128527.64
Masaharu Mizumoto2766406.85