In this paper, we discuss on-line estimation strategies that model the optimal value function of a typical optimal control problem. We present a general strategy that uses local corridor solutions obtained via dynamic programming to provide local optimal con(cid:173) trol sequence training data for a neural architecture model of the optimal value function. In this paper, the problems of adaptive control using neural architectures are ex(cid:173) plored in the setting of general on-line estimators. 'Ve will try to pay close attention to the underlying mathematical structure that arises in the on-line estimation pro(cid:173) cess. The complete effect of a control action Uk at a given time step t/.; is clouded by the fact that the state history depends on the control actions taken after time step tk' So the effect of a control action over all future time must be monitored.

We propose a very simple, and well principled way of computing the optimal step size in gradient descent algorithms. The on-line version is very efficient computationally, and is applicable to large backpropagation networks trained on large data sets. The main ingredient is a technique for estimating the principal eigenvalue(s) and eigenvector(s) of the objective function's second derivative ma(cid:173) trix (Hessian), which does not require to even calculate the Hes(cid:173) sian. Several other applications of this technique are proposed for speeding up learning, or for eliminating useless parameters.

In this paper, we discuss online estimation strategies that model the optimal value function of a typical optimal control problem. We present a general strategy that uses local corridor solutions obtained via dynamic programming to provide local optimal control sequence training data for a neural architecture model of the optimal value function.

In this paper, we discuss online estimation strategies that model the optimal value function of a typical optimal control problem. We present a general strategy that uses local corridor solutions obtained via dynamic programming to provide local optimal control sequence training data for a neural architecture model of the optimal value function.

In this paper, we discuss online estimation strategies that model the optimal value function of a typical optimal control problem. We present a general strategy that uses local corridor solutions obtained via dynamic programming to provide local optimal control sequencetraining data for a neural architecture model of the optimal value function.