Modified LMS adaptive algorithm for CMAC neural network based control of switched reluctance motors

C. Shang; D. S. Reay; B. W. Williams

Modified LMS adaptive algorithm for CMAC neural network based control of switched reluctance motors

C. Shang, D. S. Reay, B. W. Williams

School of Engineering & Physical Sciences

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

A novel approach to adapting the weights of a CMAC neural network for torque ripple reduction in switched reluctance motors is proposed, using a variable learning rate function within the standard LMS algorithm. Simulation results demonstrate that training CMAC networks following this approach affords low torque ripple with high power efficiency.

Original language	English
Pages (from-to)	1113-1115
Number of pages	3
Journal	Electronics Letters
Volume	32
Issue number	12
Publication status	Published - 6 Jun 1996

Keywords

Neurocontrollers
Reluctance motors

Cite this

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title = "Modified LMS adaptive algorithm for CMAC neural network based control of switched reluctance motors",

abstract = "A novel approach to adapting the weights of a CMAC neural network for torque ripple reduction in switched reluctance motors is proposed, using a variable learning rate function within the standard LMS algorithm. Simulation results demonstrate that training CMAC networks following this approach affords low torque ripple with high power efficiency.",

keywords = "Neurocontrollers, Reluctance motors",

author = "C. Shang and Reay, {D. S.} and Williams, {B. W.}",

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AU - Reay, D. S.

AU - Williams, B. W.

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N2 - A novel approach to adapting the weights of a CMAC neural network for torque ripple reduction in switched reluctance motors is proposed, using a variable learning rate function within the standard LMS algorithm. Simulation results demonstrate that training CMAC networks following this approach affords low torque ripple with high power efficiency.

AB - A novel approach to adapting the weights of a CMAC neural network for torque ripple reduction in switched reluctance motors is proposed, using a variable learning rate function within the standard LMS algorithm. Simulation results demonstrate that training CMAC networks following this approach affords low torque ripple with high power efficiency.

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KW - Reluctance motors

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SN - 1350-911X

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JO - Electronics Letters

JF - Electronics Letters

IS - 12

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Modified LMS adaptive algorithm for CMAC neural network based control of switched reluctance motors

Abstract

Keywords

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