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The Crystallizing Substochastic Sequential Machine Extractor: CrySSMEx
University of Skövde, School of Humanities and Informatics.
2006 (English)In: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 18, no 9, 2211-2255 p.Article in journal (Refereed) Published
Abstract [en]

This letter presents an algorithm, CrySSMEx, for extracting minimal finite state machine descriptions of dynamic systems such as recurrent neural networks. Unlike previous algorithms, CrySSMEx is parameter free and deterministic, and it efficiently generates a series of increasingly refined models. A novel finite stochastic model of dynamic systems and a novel vector quantization function have been developed to take into account the state-space dynamics of the system. The experiments show that (1) extraction from systems that can be described as regular grammars is trivial, (2) extraction from high-dimensional systems is feasible, and (3) extraction of approximative models from chaotic systems is possible. The results are promising, and an analysis of shortcomings suggests some possible further improvements. Some largely overlooked connections, of the field of rule extraction from recurrent neural networks, to other fields are also identified.

Place, publisher, year, edition, pages
MIT Press, 2006. Vol. 18, no 9, 2211-2255 p.
Identifiers
URN: urn:nbn:se:his:diva-1908DOI: 10.1162/neco.2006.18.9.2211ISI: 000239341000007PubMedID: 16846391Scopus ID: 2-s2.0-33748305559OAI: oai:DiVA.org:his-1908DiVA: diva2:32184
Available from: 2007-09-21 Created: 2007-09-21 Last updated: 2013-04-09Bibliographically approved

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