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Mixed Modified Recurring Rogers-Szego Polynomials Neural Network Control with Mended Grey Wolf Optimization Applied in SIM Expelling System

Author

Listed:
  • Der-Fa Chen

    (Department of Industrial Education and Technology, National Changhua University of Education, Changhua 500, Taiwan)

  • Yi-Cheng Shih

    (Department of Industrial Education and Technology, National Changhua University of Education, Changhua 500, Taiwan)

  • Shih-Cheng Li

    (Department of Industrial Education and Technology, National Changhua University of Education, Changhua 500, Taiwan)

  • Chin-Tung Chen

    (Graduate School of Vocational and Technological Education, National Yunlin University of Science and Technology, Yunlin 640, Taiwan)

  • Jung-Chu Ting

    (Department of Industrial Education and Technology, National Changhua University of Education, Changhua 500, Taiwan)

Abstract

Due to a good ability of learning for nonlinear uncertainties, a mixed modified recurring Rogers-Szego polynomials neural network (MMRRSPNN) control with mended grey wolf optimization (MGWO) by using two linear adjusted factors is proposed to the six-phase induction motor (SIM) expelling continuously variable transmission (CVT) organized system for acquiring better control performance. The control system can execute MRRSPNN control with a fitted learning rule, and repay control with an evaluated rule. In the light of the Lyapunov stability theorem, the fitted learning rule in the MRRSPNN control can be derived, and the evaluated rule of the repay control can be originated. Besides, the MGWO by using two linear adjusted factors yields two changeable learning rates for two parameters to find two ideal values and to speed-up convergence of weights. Experimental results in comparisons with some control systems are demonstrated to confirm that the proposed control system can achieve better control performance.

Suggested Citation

  • Der-Fa Chen & Yi-Cheng Shih & Shih-Cheng Li & Chin-Tung Chen & Jung-Chu Ting, 2020. "Mixed Modified Recurring Rogers-Szego Polynomials Neural Network Control with Mended Grey Wolf Optimization Applied in SIM Expelling System," Mathematics, MDPI, vol. 8(5), pages 1-28, May.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:5:p:754-:d:355793
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    References listed on IDEAS

    as
    1. Nagamani, G. & Ramasamy, S., 2016. "Stochastic dissipativity and passivity analysis for discrete-time neural networks with probabilistic time-varying delays in the leakage term," Applied Mathematics and Computation, Elsevier, vol. 289(C), pages 237-257.
    2. Sultana, U. & Khairuddin, Azhar B. & Mokhtar, A.S. & Zareen, N. & Sultana, Beenish, 2016. "Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system," Energy, Elsevier, vol. 111(C), pages 525-536.
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