Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1862795 | Physics Letters A | 2006 | 6 Pages |
Abstract
With regards to the ferroresonance overvoltage of neutral grounded power system, a maximum-entropy learning algorithm based on radial basis function neural networks is used to control the chaotic system. The algorithm optimizes the object function to derive learning rule of central vectors, and uses the clustering function of network hidden layers. It improves the regression and learning ability of neural networks. The numerical experiment of ferroresonance system testifies the effectiveness and feasibility of using the algorithm to control chaos in neutral grounded system.
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Physics and Astronomy (General)
Authors
Liu Fan, Sun Cai-xin, Si-ma Wen-xia, Liao Rui-jin, Guo Fei,