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

[1]  Babak Hassibi,et al.  Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.

[2]  Frank L. Lewis,et al.  Multilayer discrete-time neural-net controller with guaranteed performance , 1996, IEEE Trans. Neural Networks.

[3]  R. M. Sanner,et al.  Neural Networks for Adaptive Control and Recursive Identification: A Theoretical Framework , 1993 .

[4]  Johan A. K. Suykens,et al.  Lur'e systems with multilayer perceptron and recurrent neural networks: absolute stability and dissipativity , 1999, IEEE Trans. Autom. Control..

[5]  F. Lewis,et al.  Discrete-time neural net controller for a class of nonlinear dynamical systems , 1996, IEEE Trans. Autom. Control..

[6]  Qing Song,et al.  Robust Adaptive Dead Zone Technology for Fault-Tolerant Control of Robot Manipulators Using Neural Networks , 2002, J. Intell. Robotic Syst..

[7]  Qing Song,et al.  Dynamic pruning algorithm for multilayer perceptron based neural control systems , 2006, Neurocomputing.

[8]  Chein-I Chang,et al.  Robust radial basis function neural networks , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Qing Song,et al.  Robust backpropagation training algorithm for multilayered neural tracking controller , 1999, IEEE Trans. Neural Networks.

[10]  John C. Platt.  A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.

[11]  Qing Song,et al.  Pruning Based Robust Backpropagation Training Algorithm for RBF Network Tracking Controller , 2007, J. Intell. Robotic Syst..

[12]  Sarangapani Jagannathan,et al.  Control of a class of nonlinear discrete-time systems using multilayer neural networks , 2001, IEEE Trans. Neural Networks.

[13]  Gene H. Golub,et al.  Matrix computations , 1983 .

[14]  Marios M. Polycarpou,et al.  Learning and convergence analysis of neural-type structured networks , 1992, IEEE Trans. Neural Networks.

[15]  Marimuthu Palaniswami,et al.  An adaptive tracking controller using neural networks for a class of nonlinear systems , 1998, IEEE Trans. Neural Networks.