Title :
Design for Self-Organizing Fuzzy Neural Networks Based on Evolutionary Programming
Author_Institution :
Sch. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China
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 :
evolutionary computation; fuzzy neural nets; learning (artificial intelligence); self-adjusting systems; Takagi-Sugeno type fuzzy models; evolutionary programming; fuzzy rule base; hybrid learning algorithm; self-organizing fuzzy neural networks design; Algorithm design and analysis; Biological neural networks; Computational modeling; Computer networks; Computer simulation; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Genetic programming; Neurons; Fuzzy Neural Networks; evolutionary programming; fuzzy rule;
Conference_Titel :
Computer Modeling and Simulation, 2010. ICCMS '10. Second International Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-1-4244-5642-0
Electronic_ISBN :
978-1-4244-5643-7
DOI :
10.1109/ICCMS.2010.108