Title :
Using hysteresis to improve performance in synchronous networks
Author :
Anand, Tirunelveli ; Minai, Ali A.
Author_Institution :
Complex Adaptive Syst. Lab., Cincinnati Univ., OH, USA
Abstract :
In this paper, we present a signal-to-noise analysis of synchronous attractor networks of hysteretic threshold elements. The addition of hysteresis is known to enhance the capacity and convergence rate of attractor networks. The aim of this paper is partly to elaborate on results reported previously by other researchers, and to address the issue of whether there is an optimal value for hysteresis. Based on the simplified analysis reported here, we conclude that, for a given network loading (ratio of patterns stored to network size), there is an optimal value of hysteresis, but it changes as recovery proceeds to convergence. We hypothesize that a time-varying “hysteresis schedule” can be used to enhance the performance of attractor networks
Keywords :
content-addressable storage; hysteresis; neural nets; associative memories; convergence rate; hysteretic threshold elements; network loading; neural nets; performance improvement; signal-to-noise analysis; synchronous attractor networks; time-varying hysteresis schedule; Adaptive systems; Associative memory; Convergence; Hysteresis; Intelligent networks; Laboratories; Neurons; Noise reduction; Pattern analysis; Signal analysis;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.616206