Using perturbation analysis for gradient estimation, averaging and updating in a stochastic approximation algorithm

We propose a gradient updating procedure for using both "present" and "past" data to improve the convergence properties of a stochastic approximation algorithm. This procedure utilizes second derivatives estimated by perturbation analysis techniques. Experimental evidence piovided by simulation runs appear to confirm the improvement in convergence rate gained by this modified algorithm.