9309057 Si In system control, a mathematical description of a process is often a prerequisite to the controller design. The system identification techniques for linear systems are new well understood nonlinear systems due to the inherent complexity of nonlinear systems and the difficulty of deriving identification algorithms. These difficulties are severed in building empirically based models because of unavoidable noise in real-world applications. This project will use recurrent neural networks to perform nonlinear system identification. The parameters defining the network are determined through input-output data training. The methodology for training is essentially gradient- based. Stability methods will also be examined for two reasons. It provides a qualitative measure of identification performance and provides some insight as t improve the efficiency of gradient methods. These approaches themselves are not unique until real-world application criteria (lower error rates, immune to noise etc.) are taken into consideration. To account for noise effect from empirical data, robust norm will be considered as a measure of parameter prediction error. Theoretical analysis based on adaptive systems theory and statistical analysis will be employed to gain qualitative understanding of the proposed problem. Numerical simulation will be carried out to test proposed identification procedures. The goal of both theoretical and empirical approaches are the same: reveal unexplored aspects of neural networks for real world problems. The proposed research is inspired and will be tested by an industrial scale process identification problem. In particular the experiments will be conducted at a Honeywell TDC-3000 real time plant information and control system interfaced to a methanol- isopropanil distillation column. ***
