Optimization with neural memory for process parameter estimation

The speed and accuracy of convergence of iterative optimization algorithms often depend critically upon the choice of a starling point. With a near optimum starting point, both speed and accuracy can be improved. A two step approach to optimization has been developed which utilizes the feedforward predictive capability of a neural network in conjunction with the feedback capability of an iterative optimization algorithm. This approach is taken in order to improve the speed of the iterative optimization algorithm, and also enhance the iterative algorithm's ability to locate a global optimum. This technique has been applied to the problem of system identification for continuous time transfer function models. The neural network is used to select an initial set of process parameters for a given model structure using unit step response data. We present results on the accuracy of the predictive capability of the neural network, and results showing the improved performance of the iterative nonlinear system identification algorithm.