A Novel Model, Recurrent Fuzzy Associative Memory, for Recognizing Time-Series Patterns Contained Ambiguity and Its Application


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 4, pp. 449-456, Apr. 2004
10.3745/KIPSTB.2004.11.4.449, Full Text:

Abstract

This paper proposes a novel recognition model, a recurrent fuzzy associative memory(RFAM), for recognizing time-series patterns contained an ambiguity. RFAM is basically extended from FAM(Fuzzy Associative memory) by adding a recurrent layer which can be used to deal with sequential input patterns and to characterize their temporal relations. RFAM provides a Hebbian-style learning method which establishes the degree of association between input and output. The error back-propagation algorithm is also adopted to train the weights of the recurrent layer of RFAM. To evaluate the performance of the proposed model, we applied it to a word boundary detection problem of speech signal.


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Cite this article
[IEEE Style]
W. Kim, J. J. Lee, G. Y. Kim and H. I. Choi, "A Novel Model, Recurrent Fuzzy Associative Memory, for Recognizing Time-Series Patterns Contained Ambiguity and Its Application," KIPS Journal B (2001 ~ 2012) , vol. 11, no. 4, pp. 449-456, 2004. DOI: 10.3745/KIPSTB.2004.11.4.449.

[ACM Style]
Won Kim, Joong Jae Lee, Gye Young Kim, and Hyung Il Choi. 2004. A Novel Model, Recurrent Fuzzy Associative Memory, for Recognizing Time-Series Patterns Contained Ambiguity and Its Application. KIPS Journal B (2001 ~ 2012) , 11, 4, (2004), 449-456. DOI: 10.3745/KIPSTB.2004.11.4.449.