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Computational Intelligence and Neuroscience
Volume 2016, Article ID 7158507, 14 pages
http://dx.doi.org/10.1155/2016/7158507
Research Article

A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State

Department of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300387, China

Received 31 May 2016; Revised 23 July 2016; Accepted 27 July 2016

Academic Editor: Manuel Graña

Copyright © 2016 Zuo-wei Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Identifying the hidden state is important for solving problems with hidden state. We prove any deterministic partially observable Markov decision processes (POMDP) can be represented by a minimal, looping hidden state transition model and propose a heuristic state transition model constructing algorithm. A new spatiotemporal associative memory network (STAMN) is proposed to realize the minimal, looping hidden state transition model. STAMN utilizes the neuroactivity decay to realize the short-term memory, connection weights between different nodes to represent long-term memory, presynaptic potentials, and synchronized activation mechanism to complete identifying and recalling simultaneously. Finally, we give the empirical illustrations of the STAMN and compare the performance of the STAMN model with that of other methods.