Learning a Markov process with a synchronous Boltzmann machine

Abstract

In this paper we present the simulations of a synchronous Boltzmann machine which learns to model a Markov process. The advantage in using synchronous updating is lies in the parallelism that the model offers. Learning with synchronous Boltzmann machines provides an attractive alternative to asynchronous learning provided that one can establish a suitable theoretical framework. The dynamics of synchronous Boltzmann machines were first studied by W.A. Little [Little 1974], [Little 1978] and [Perreto 1984]. The aim of the present study is to present the results generated by a new local learning algorithm for synchronous Boltzmann machines. The algorithm uses Gradient-Descent to update weights and thresholds. Three different Markov processes were set to be modelled with a three unit network.