A PARAMETER DISTURBING ALGORITHM FOR NEURAL NETWORKS AND ITS APPLICATION TO MANIPULATOR CONTROL

In this paper, a parameter disturbing algorithm of neural networks which can overcome the local minima problem of Hopfield network is proposed on the basis of considering the gradient convergence of Hopfield network and the principle of stochastic simulated annealing developed by Kirpatrick. It is of similar characteristics of stochastic annealing with typical SA algorithm, whose performance is analysed in terms of digital simulations in detail. Finally, the proposed algorithm is successfully applied to the inverse kinematics problem of manipulator control, and then a neural network solver for the problem is established. It has been shown by digital simulations that the proposed neural network control method not only has fast control speed but also raises the control accuracy of manipulator.