State Automata Extraction from Recurrent Neural Nets using k-Means and Fuzzy Clustering

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	Similar Articles Articles by Adelmo Luis Cechin Articles by Denise Regina Pechmann Simon Articles by Klaus Stertz

XXIII International Conference of the Chilean Computer Science Society

Chill?n, Chile

November 06-November 07

ISBN: 0-7695-2008-1

	ASCII Text	x
	Adelmo Luis Cechin, Denise Regina Pechmann Simon, Klaus Stertz, "State Automata Extraction from Recurrent Neural Nets using k-Means and Fuzzy Clustering," Chilean Computer Science Society, International Conference of the, pp. 73, XXIII International Conference of the Chilean Computer Science Society, 2003.

	BibTex	x
	@article{ 10.1109/SCCC.2003.1245447, author = {Adelmo Luis Cechin and Denise Regina Pechmann Simon and Klaus Stertz}, title = {State Automata Extraction from Recurrent Neural Nets using k-Means and Fuzzy Clustering}, journal ={Chilean Computer Science Society, International Conference of the}, volume = {0}, year = {2003}, issn = {1522-4902}, pages = {73}, doi = {http://doi.ieeecomputersociety.org/10.1109/SCCC.2003.1245447}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Chilean Computer Science Society, International Conference of the TI - State Automata Extraction from Recurrent Neural Nets using k-Means and Fuzzy Clustering SN - 1522-4902 SP EP A1 - Adelmo Luis Cechin, A1 - Denise Regina Pechmann Simon, A1 - Klaus Stertz, PY - 2003 KW - null VL - 0 JA - Chilean Computer Science Society, International Conference of the ER -

Adelmo Luis Cechin, UNISINOS University, Brasil

Denise Regina Pechmann Simon, UNISINOS University, Brasil

Klaus Stertz, UNISINOS University, Brasil

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SCCC.2003.1245447

This paper presents the use of a recurrent neural network to learn the dynamical behavior of the inverted pendulum and from this network to extract a finite state automata. Two clustering methods are compared for the automata extraction: the K-means method, and the construction of fuzzy membership functions. It is shown that the number of states for the fuzzy clustering method induces much less states than the K-means method.

Citation:

Adelmo Luis Cechin, Denise Regina Pechmann Simon, Klaus Stertz, "State Automata Extraction from Recurrent Neural Nets using k-Means and Fuzzy Clustering," sccc, pp.73, XXIII International Conference of the Chilean Computer Science Society, 2003

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