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Title: Accurate modeling of switched reluctance machine based on hybrid trained WNN

According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, the nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.
Authors:
; ; ;  [1]
  1. School of Automation, Northwestern Polytechnical University, Xi'an 710072 (China)
Publication Date:
OSTI Identifier:
22253038
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Advances; Journal Volume: 4; Journal Issue: 4; Other Information: (c) 2014 Author(s); Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; ACCURACY; ALGORITHMS; CONVERGENCE; DETERMINISTIC ESTIMATION; EVALUATION; NEURAL NETWORKS; SIMULATION