A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State
Author
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-03
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Zuo-wei. 2016. A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1099746
Modern Language Association (MLA)
Wang, Zuo-wei. A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-14.
https://search.emarefa.net/detail/BIM-1099746
American Medical Association (AMA)
Wang, Zuo-wei. A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1099746
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099746