Applications of neural networks for switched reluctance machine drive

被引：0

作者：

Chen, H. ^{[1
]}

Sun, C. ^{[1
]}

Liu, J. ^{[1
]}

机构：

[1] China Univ Min & Technol, Coll Informat & Elect Engn, Xuzhou 221008, Peoples R China

来源：

DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS | 2007年 / 14卷

关键词：

switched reluctance; neural network; simulation;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The four-phase 8/6 structure Switched Reluctance revolving machine, the four-phase asymmetric bridge power converter, the three-phase 6/4 structure double sides Switched Reluctance linear machine and the three-phase asymmetric bridge power converter were described as the researched two types of the prototypes. The principles of the radial basis function neural networks adopted the Gauss function were given with the model of the radial basis nerve cell and the model of the radial basis neural networks. The trained results of the curves of the rotor position and the phase current to the magnetic flux links, the curves of the rotor position and the phase current to the magnetic coenergy in the four-phase 8/6 structure Switched Reluctance revolving machine were presented. The trained results of the curves of the rotor position to the phase current and the rotor position to the electromagnetic force in the three-phase 6/4 structure double sides Switched Reluctance linear machine were also presented. It is shown that the trained curves tallies with the swatches, and the more the swatch is, the better the trained results are.

引用

页码：839 / 843

页数：5