Neural Modeling and Identification of Nonlinear Systems in an Abstract Space Setting

Abstract

A rigorous framework is available for the modeling and identification of nonlinear dynamical systems by artificial neural networks. The system model is obtained as a best approximation of the operator(s) representing the system in a “neural space”, under interpolating or smoothing constraints imposed by the input-output training data. This optimal modeling results in one of four types of neural networks proposed and discussed by the author elsewhere, namely the OI, OS, OMNI and OSMAN nets. The identification of a system so modeled can take place instantaneously by batch processing of the training data, or sequentially by adaptation, learning, and/or evolution. These concepts are briefly discussed in the present article, and illustrated in terms of a self-tuning nonlinear regulator system.