Robust pruning of RBF network for neural tracking control systems

Ni, J. and Song, Q. and Grimble, M.J. (2006) Robust pruning of RBF network for neural tracking control systems. In: 45th IEEE Conference on Decision and Control, 2006-12-13 - 2006-12-15. (https://doi.org/10.1109/CDC.2006.377039)

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Abstract

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.

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