Robust pruning of RBF network for neural tracking control systems

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.