DocumentCode
2251813
Title
Differential evolution algorithem design for fuzzy neural network
Author
Ma, Ming ; Sun, Yan ; Zhang, Li-Biao
Author_Institution
Inf. Manage Center, Beihua Univ., Jilin, China
Volume
3
fYear
2010
fDate
11-14 July 2010
Firstpage
1443
Lastpage
1446
Abstract
Differential evolution is a novel method to search global optimum. A new pruning algorithm for solving the fuzzy neural network design problem is proposed based on differential evolution with division of work. Based on the proposed algorithm, an optimal and efficient fuzzy neural network structure can be constructed by the requirements. Numerical simulations show the effectiveness of the proposed algorithm.
Keywords
evolutionary computation; fuzzy neural nets; differential evolution algorithem design; fuzzy neural network; fuzzy neural network structure; optimum search; Algorithm design and analysis; Brain modeling; Evolutionary computation; Fuzzy neural networks; Machine learning; Optimization; Signal processing algorithms; Differential evolution; Fuzzy neural network; Fuzzy rule;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location
Qingdao
Print_ISBN
978-1-4244-6526-2
Type
conf
DOI
10.1109/ICMLC.2010.5580834
Filename
5580834
Link To Document