Mixed modified recurring Rogers-Szego polynomials neural network control with mended grey wolf optimization applied in SIM expelling system

Der Fa Chen, Yi Cheng Shih, Shih Cheng Li, Chin Tung Chen, Jung Chu Ting

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number754
JournalMathematics
Volume8
Issue number5
DOIs
Publication statusPublished - 2020 May 1

All Science Journal Classification (ASJC) codes

  • Mathematics(all)

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