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 :
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