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Nonlinear Modeling of Switched Reluctance Motor Based on GA-BPNN

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Emerging Research in Artificial Intelligence and Computational Intelligence (AICI 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 315))

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Abstract

Nonlinear modeling of the flux linkage characteristics is fundamental to the control design and performance evaluation of switched reluctance motor (SRM). Conventional back propagation neural network (BPNN) modeling method has demerits of local minimum which significantly slow the convergence rate. So, genetic algorithm (GA) is introduced to overcome the local minimum of BPNN in this paper. And GA is applied to train the weighs and bias for BPNN. And Based on the flux linkage characteristics obtained from finite element method (FEM) of a 16/12 SRM, a nonlinear flux linkage model using GA-BPNN is set up, and simulation results demonstrate that the model has fast convergence rate, high accuracy, and strong generalization ability.

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Author information

Authors and Affiliations

  1. Chongqing Communication Institute, Chongqing, 400035, China

    Ruiwei Zhang, Yingchao Zhang & Xishen Qian

Authors
  1. Ruiwei Zhang
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  2. Yingchao Zhang
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  3. Xishen Qian
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Editor information

Editors and Affiliations

  1. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  2. Department of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

  3. School of Business Information Technology, RMIT University, City Campus, 124 La Trobe Street, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  4. School of Electronics and Information, Tongji University, China, 201804, Shanghai, China

    Duoqian Miao

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© 2012 Springer-Verlag Berlin Heidelberg

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Zhang, R., Zhang, Y., Qian, X. (2012). Nonlinear Modeling of Switched Reluctance Motor Based on GA-BPNN. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Emerging Research in Artificial Intelligence and Computational Intelligence. AICI 2012. Communications in Computer and Information Science, vol 315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34240-0_7

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  • DOI: https://doi.org/10.1007/978-3-642-34240-0_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34239-4

  • Online ISBN: 978-3-642-34240-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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