System And Method For A Learning Neural Network For Generating Random Directions For Weight Changes

Abstract

A learning neural network (30) implements a random weight change learning algorithm within a weight adjustment mechanism (28) for manipulating the weights applied to inputs of the network (30) in order to achieve a desired functionality for the network (30). Weights are changed randomly from an initial state with a small incremental weight change of either +ẟ or -ẟ. If the overall network output decreases by the weight change, the same weight change is iterated until the error increases. If, however, the overall network error increases, the weights are changed randomly again. After iterating the foregoing methodology, the network error gradually decreases and finally reaches approximately zero. Furthermore, a shift mechanism (36) and a multiplier (38) are employed as a weight application mechanism (16). The shift mechanisms (36) are connected in series with a random line (35) and are connected in parallel with a shift line (44). A random direction is successively channelled through the shift mechanisms (36) via the random line (35) under the control of the shift line ( 44) so that only a single random number need be generated for all of the shift mechanisms (36) within the neural network (30).