Application research of modeling method based on neural networks with parallel chaotic search

In view of nonlinear characteristic of switched reluctance motor and existing modeling method has shortcomings of random initial weights of network parameters and is easy to fall into local minimum point, the paper put forward a modeling method using parallel optimization chaotic and BP neural network. Firstly, the method uses chaotic system to optimize neural network weight vector and initial threshold vector, and then uses Levenberg-Marquardt algorithm of BP neural network to train convergence. If it drops into the local minimum point, then it needs to use parallel chaotic search to optimize model again, so as to make the model have characteristics of high precision and fast speed. The dynamic simulation results of training model and speed-regulation system of switched reluctance motor show that the model established by the method has stable operation, good dynamic performance, and fast response speed.