A NEURAL NETWORK APPROACH TO ON-LINE IDENTIFICATION OF NONLINEAR SYSTEMS

Research investigating neural identification of dynamic systems has concentrated on off-line techniques. To be suitable for adaptive process control, on-line algorithms must be developed. This study investigates a modified back-propagation technique to achieve practical on-line capability. A technique denoted history-stack enhancement greatly improves the identification performance of the neural network. As a demonstration, a composite system of formidable but realistic nonlinear components was constructed and used to compare identification techniques including a recursive linear estimator and die new neural method. The results show that on-line neural identification is feasible for a wide range of formidable nonlinear characteristics individually found in industrial processes. Although performance is slower than with linear identification, the asymptotic accuracy of the neural technique is better for the nonlinear system being identified.