Adaptive nonlinear system identification using minimal radial basis function neural networks

In this paper, an adaptive identification scheme for nonlinear systems using a minimal radial basis function neural network (RBFNN) is presented. This scheme combines the growth criterion of the resource-allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. While being applied to nonlinear system identification, this approach enables the number of hidden layer neurons in the network to be adjusted to the changing system dynamics, the resulting neural network also leads to a minimal topology for the RBFNN. Simulations are carried out to recursively identify two nonlinear systems with time-varying dynamics. The performance of the proposed algorithm is compared with the recursive hybrid algorithm for system identification proposed by Chen et al. (1992). The proposed algorithm in this paper is shown to realize a RBFNN with far fewer hidden neurons and better accuracy.