Nonlinear system identification using a Gabor/Hopfield network

This paper presents a method of nonlinear system identification using a new Gabor/Hopfield network. The network can identify nonlinear discrete-time models that are affine linear in the control. The system need not be asymptotically stable but must be bounded-input-bounded-output (BIBO) stable for the identification results to be valid in a large input-output range. The network is a considerable improvement over earlier work using Gabor basis functions (GBF´s) with a back-propagation neural network. Properties of the Gabor model and guidelines for achieving a global error minimum are derived. The new network and its use in system identification are investigated through computer simulation. Practical problems such as local minima, the effects of input and initial conditions, the model sensitivity to noise, the sensitivity of the mean square error (MSE) to the number of basis functions and the order of approximation, and the choice of forcing function for training data generation are considered