Modeling of switched reluctance motors based on optimized BP neural networks with parallel chaotic search

Precise modeling of switched reluctant motor (SRM) is important of switched reluctant motor driving system. In the article, modeling of SRM by a BP neural network with parallel chaotic search (PCS) is presented firstly. Here parallel chaotic search is proposed to optimize vectors of weight and threshold. Modified BP neural network has been improved in convergence, generalizing and network scale for real time control. Based on the results of simulation, the nonlinear modeling of SRM has performed better, which has faster convergence and improved in efficiency.