Name
Abstract | ||
|---|---|---|
Traditional fuzzy neural networks have many problems like long duration of training learning algorithm, slow convergence and possibility of getting into local dinky value in the network. In view of all these above, this paper proposes an improved training algorithm in fuzzy neural network which takes vantage of the excellent learning and expression ability of fuzzy neural networks based on T-S (Takagi-Sugeno) model. Improved T-S model introduces adaptive learning rate *** and momentum factor *** into the learning algorithm which helps stabilize the network and makes acceleration weights of three adjusting parameters, including network membership function's central value C ij and width *** ij and connection weight of output layer y kj , decrease along the reductive direction. Results of a simulation test with a two-dimensional nonlinear function show that improved network has high convergence rate and good generalization capability. Finally, T-S fuzzy model of hydro-turbine governing system based on the fuzzy neural network is presented, of which the results has indicated that the model has/its higher precision and good generalization ability. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-01510-6_18 | ISNN (2) |
Keywords | Field | DocType |
central value,improved t-s model,fuzzy neural network,improved network,c ij,improved training algorithm,excellent learning,network membership function,t-s fuzzy model,traditional fuzzy neural network,system identification,function approximation,convergence rate,membership function | Neuro-fuzzy,Defuzzification,Fuzzy classification,Computer science,Wake-sleep algorithm,Artificial intelligence,Adaptive neuro fuzzy inference system,Artificial neural network,Fuzzy number,Membership function,Machine learning | Conference |
Volume | ISSN | Citations |
5552 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiwei Huang | 1 | 0 | 0.34 |
| Jianzhong Zhou | 2 | 511 | 55.54 |
| Chaoshun Li | 3 | 42 | 7.51 |
| Fengpan Li | 4 | 0 | 0.34 |
| Yongchuan Zhang | 5 | 183 | 21.99 |