Paper Info
Title
Hierarchical genetic fuzzy systems
Abstract
This paper introduces a hierarchical evolutionary approach to optimize the parameters of Takagi–Sugeno (TS) fuzzy systems. The approach includes a least-squares method to determine the parameters of nonlinear consequents. A pruning procedure is developed to avoid redundancy in each rule consequent and to achieve proper representation flexibility. The performance of the hierarchical evolutionary approach is evaluated using function approximation and classification problems. They demonstrate that the evolutionary algorithm, working together with optimization and pruning procedures, provides structurally simple fuzzy systems whose performance seems to be better than the ones produced by alternative approaches.
Year
DOI
Venue
2001
10.1016/S0020-0255(01)00140-2
Inf. Sci.
Keywords
Field
DocType
hierarchical genetic fuzzy system,function approximation,evolutionary algorithm,fuzzy system,least square method
Mathematical optimization,Function approximation,Evolutionary algorithm,Fuzzy classification,Fuzzy set operations,Redundancy (engineering),Artificial intelligence,Fuzzy control system,Fuzzy number,Mathematics,Machine learning,Genetic fuzzy systems
Journal
Volume
Issue
ISSN
136
1-4
0020-0255
Citations 
PageRank 
References 
23
1.17
6
Authors
3
Name
Order
Citations
PageRank
Myriam Regattieri Delgado122422.26
Fernando Von Zuben2614.04
Fernando Gomide363149.76