DocumentCode
518596
Title
Design for self-organizing fuzzy neural networks based on adaptive evolutionary programming
Author
Liu Fang
Author_Institution
Sch. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China
Volume
3
fYear
2010
fDate
27-29 March 2010
Firstpage
251
Lastpage
254
Abstract
A novel hybrid learning algorithm based on a evolutionary programming to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on evolutionary programming, to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. construct and parameters of the fuzzy neural network is trained by evolutionary algorithms. Simulation results demonstrate that a compact and high performance fuzzy rule base can be constructed. Comprehensive comparisons with other approach show that the proposed approach is superior over other in terms of learning efficiency and performance.
Keywords
adaptive systems; evolutionary computation; fuzzy neural nets; learning (artificial intelligence); self-adjusting systems; Takagi-Sugeno type fuzzy model; adaptive evolutionary programming; hybrid learning algorithm; self-organizing fuzzy neural network; Adaptive systems; Algorithm design and analysis; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Genetic programming; Neural networks; Neurons; Partitioning algorithms; Takagi-Sugeno model; Fuzzy Neural Networks; evolutionary programming; fuzzy rule;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location
Shenyang
Print_ISBN
978-1-4244-5845-5
Type
conf
DOI
10.1109/ICACC.2010.5486626
Filename
5486626
Link To Document