Robot control using neural networks with adaptive learning steps

Wanek Golnazarian; Richard Shell; Ernest L. Hall

doi:10.1117/12.131592

1 November 1992 Robot control using neural networks with adaptive learning steps

Wanek Golnazarian, Richard Shell, Ernest L. Hall

Proceedings Volume 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods; (1992) https://doi.org/10.1117/12.131592
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

Abstract

Artificial neural networks (ANNs) are highly parallel, adaptive and fault tolerant dynamical systems modeled like their biological counterparts. Given a set of input-output patterns, ANNs can learn to classify these patterns by optimizing the weights connecting the nodes of the networks. The backpropagation (BP) algorithm using the gradient search technique has been a popular method for training artificial neural networks. However, the BP method, in which each step size is fixed at an arbitrary value, frequently experiences cycling and often falls in a local minimum instead of finding the desired global minimum of the error function. In this paper, an ANN utilizing an adaptive step size algorithm based on random search techniques is proposed to solve the inverse kinematic problem in robotics. This procedure assures monotonic convergence by adjusting the step size on each step based on the net gradient and the direction of the steepest descent. The results of the computer simulation for the improved adaptive method show a much better convergence rate and robustness than the BP method. This improvement can minimize the burden of real time processing load for robot control by reducing the costly derivation and computationally intense programming of the inverse kinematic algorithm.

Citation Download Citation

Wanek Golnazarian, Richard Shell, and Ernest L. Hall "Robot control using neural networks with adaptive learning steps", Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); https://doi.org/10.1117/12.131592

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