Robust pruning of RBF network for neural tracking control systems

J. Ni, Q. Song, M.J. Grimble

Research output: Contribution to conferencePaper


It is difficult to determine the number of nodes that should be used in a neural network. An adaptive method is proposed whereby the initial select is based on the largest expected number and the algorithm then "prunes" the numbers. A robust backpropagation training algorithm is proposed for the online tuning of a radial basis function(RBF) network tracking control system. The structure of the RBF network controller is derived using a filtered error approach. The proposed pruning method in this paper begins with a relatively large network, and certain neural units of the RBF network are dropped by examining the estimation error increment. A complete convergence proof is provided in the presence of disturbances.
Original languageEnglish
Number of pages6
Publication statusPublished - 2006
Event45th IEEE Conference on Decision and Control - San Diego, United States
Duration: 13 Dec 200615 Dec 2006


Conference45th IEEE Conference on Decision and Control
Country/TerritoryUnited States
CitySan Diego


  • neural tracking control systems
  • control systems
  • neural tracking


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