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A Learning Algorithm of CMAC Based on RLS

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Abstract

Conventionally, least mean square rule which can be named CMAC-LMS is used to update the weights of CMAC. The convergence ability of CMAC-LMS is very sensitive to the learning rate. Applying recursive least squares (RLS) algorithm to update the weights of CMAC, we bring forward an algorithm named CMAC-RLS. And the convergence ability of this algorithm is proved and analyzed. Finally, the application of CMAC-RLS to control nonlinear plant is investigated. The simulation results show the good convergence performance of CMAC-RLS. The results also reveal that the proposed CMAC-PID controller can reject disturbance effectively, and control nonlinear time-varying plant adaptively.

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Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, Hefei, 230027, China

    Ting Qin, Zonghai Chen, Haitao Zhang, Sifu Li, Wei Xiang & Ming Li

Authors
  1. Ting Qin
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  2. Zonghai Chen
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  3. Haitao Zhang
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  4. Sifu Li
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  5. Wei Xiang
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  6. Ming Li
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Qin, T., Chen, Z., Zhang, H. et al. A Learning Algorithm of CMAC Based on RLS. Neural Processing Letters 19, 49–61 (2004). https://doi.org/10.1023/B:NEPL.0000016847.18175.60

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  • DOI: https://doi.org/10.1023/B:NEPL.0000016847.18175.60

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