Feed-back neural networks with discrete weights

Authors

Lijuan Wang, University of WollongongFollow
Qingguo Zhou, Lanzhou University
Tao Jin, Xiamen University
Hong Zhao, Xiamen University

RIS ID

77970

Publication Details

Wang, L., Zhou, Q., Jin, T. & Zhao, H. (2013). Feed-back neural networks with discrete weights. Neural Computing and Applications, 22 (6), 1063-1069.

Abstract

We use the Monte Carlo Adaptation learning algorithm to design feed-back neural networks with discrete weights. The dynamic properties of these types of neural networks are investigated as a function of the states of weights. The numerical results of these networks show three phases: the"chaos phase", the "pure memory phase"and the "mixture phase" in the parameter space. The maximum storage ratio for the "pure memory phase" increases with the increasing of the states of the weights, which is favorable for practical applications.