Sage Revised Reiterative Even Zernike Polynomials Neural Network Control with Modified Fish School Search Applied in SSCCRIM Impelled System

Chih-Hong Lin
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Chih-Hong Lin: Department of Electrical Engineering, National United University, Miaoli 360, Taiwan

Mathematics, 2020, vol. 8, issue 10, 1-30

Abstract: In light of fine learning ability in the existing uncertainties, a sage revised reiterative even Zernike polynomials neural network (SRREZPNN) control with modified fish school search (MFSS) method is proposed to control the six-phase squirrel cage copper rotor induction motor (SSCCRIM) impelled continuously variable transmission assembled system for obtaining the brilliant control performance. This control construction can carry out the SRREZPNN control with the cozy learning law, and the indemnified control with an assessed law. In accordance with the Lyapunov stability theorem, the cozy learning law in the revised reiterative even Zernike polynomials neural network (RREZPNN) control can be extracted, and the assessed law of the indemnified control can be elicited. Besides, the MFSS can find two optimal values to adjust two learning rates with raising convergence. In comparison, experimental results are compared to some control systems and are expressed to confirm that the proposed control system can realize fine control performance.

Keywords: even Zernike polynomials neural network; fish school search; Lyapunov stability theorem; six-phase squirrel cage copper rotor induction motor (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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