Name
Abstract | ||
|---|---|---|
A new fuzzy neural network is introduced in this paper which employs self-organization competition neural network to optimize the structure of the fuzzy neural network, and applies a genetic algorithm to adjust the connection weights of the fuzzy neural network so as to get the best structure and weights of the fuzzy neural network. Simulations are made when the pole becomes 2 meters and the random white noise is added in the cart-pendulum system, and control effects of the Adaptive Neural Fuzzy Illation System (ANFIS) and Genetic Algorithm Fuzzy Neural Network (GAFNN) are analyzed. Simulation results indicate that GAFNN controller has greater control performance, high convergence speed, strong robustness and better dynamic characteristics. The effectiveness of the method introduced in this paper is demonstrated by its encouraging study results. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74769-7_72 | LSMS (1) |
Keywords | Field | DocType |
genetic algorithm fuzzy neural,new fuzzy neural network,fuzzy neural network,gafnn controller,adaptive neural fuzzy illation,greater control performance,cart-pendulum system,new-type fuzzy-neural network controller,control effect,best structure,self-organization competition neural network,self organization,white noise,genetic algorithm,neural network | Neuro-fuzzy,Stochastic neural network,Fuzzy logic,Recurrent neural network,Probabilistic neural network,Time delay neural network,Artificial intelligence,Adaptive neuro fuzzy inference system,Engineering,Artificial neural network | Conference |
Volume | ISSN | ISBN |
4688 | 0302-9743 | 3-540-74768-0 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiucheng Dong | 1 | 4 | 2.61 |
| Haibin Wang | 2 | 61 | 13.43 |
| Qiang Xu | 3 | 7 | 4.54 |
| Xiaoxiao Zhao | 4 | 2 | 0.82 |