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State Automata Extraction from Recurrent Neural Nets using k-Means and Fuzzy Clustering
Chill?n, Chile November 06-November 07
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SCCC.2003.1245447XXIII International Conference of the ...
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Adelmo Luis Cechin, UNISINOS University, Brasil
Denise Regina Pechmann Simon, UNISINOS University, Brasil
Klaus Stertz, UNISINOS University, Brasil
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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