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

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3 Author(s)
Shang, C. ; Dept. of Comput. & Electr. Eng., Heriot-Watt Univ., Edinburgh, UK ; Reay, D.S. ; Williams, B.W.

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

Published in:

Electronics Letters  (Volume:32 ,  Issue: 12 )