A Learning Algorithm of CMAC Based on RLS

  • Authors:
  • Ting Qin;Zonghai Chen;Haitao Zhang;Sifu Li;Wei Xiang;Ming Li

  • Affiliations:
  • Department of Automation, University of Science and Technology of China, Hefei 230027, China. e-mail: dragon@mail.ustc.edu.cn;Department of Automation, University of Science and Technology of China, Hefei 230027, China. e-mail: dragon@mail.ustc.edu.cn;Department of Automation, University of Science and Technology of China, Hefei 230027, China. e-mail: dragon@mail.ustc.edu.cn;Department of Automation, University of Science and Technology of China, Hefei 230027, China. e-mail: dragon@mail.ustc.edu.cn;Department of Automation, University of Science and Technology of China, Hefei 230027, China. e-mail: dragon@mail.ustc.edu.cn;Department of Automation, University of Science and Technology of China, Hefei 230027, China. e-mail: dragon@mail.ustc.edu.cn

  • Venue:
  • Neural Processing Letters
  • Year:
  • 2004

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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.