Title :
A similarity-based learning algorithm for fuzzy system identification with a two-layer optimization scheme
Author :
Lee, Shin-Jye ; Zeng, Xiao-Jun
Author_Institution :
Judge Bus. Sch., Univ. of Cambridge, Cambridge, UK
Abstract :
This paper presents a similarity-based fuzzy learning approach with a two-layer optimization scheme to make fuzzy systems more compact and accuracy. Two ways to improve fuzzy learning algorithms are considered in this paper, including the pruning strategy for simplifying the structure of fuzzy systems and the optimization scheme for parameters optimization. So far as the pruning strategy is concerned, the purpose aims at refining the fuzzy rule base by the similarity analysis of fuzzy sets, fuzzy numbers, fuzzy membership functions or fuzzy rules. Through the similarity analysis, the complete rules can be probably kept by decreasing the redundant rules in the rule base of fuzzy systems. Moreover, the optimization scheme can be regarded as a two-layer parameters optimization in the entire work, because the parameters of the initial fuzzy model have been fine tuning by two phases gradation on layer for discovering a better local minimum.
Keywords :
fuzzy reasoning; fuzzy set theory; learning (artificial intelligence); optimisation; fuzzy membership function; fuzzy number; fuzzy rule base; fuzzy set; fuzzy system identification; pruning strategy; redundant rule; similarity analysis; similarity-based fuzzy learning algorithm; two-layer parameters optimization; Accuracy; Clustering algorithms; Fuzzy sets; Fuzzy systems; Input variables; Optimization; Testing; fuzzy set; fuzzy system identification; optimization; similarity analysis;
Conference_Titel :
Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1507-4
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZ-IEEE.2012.6251330