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Multilayer perceptron, fuzzy sets, and classificationA fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.
Document ID
19930047271
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Pal, Sankar K.
(Indian Statistical Inst., Calcutta, India; NASA, Johnson Space Center Houston, TX, United States)
Mitra, Sushmita
(Indian Statistical Inst. Calcutta, India)
Date Acquired
August 16, 2013
Publication Date
September 1, 1992
Publication Information
Publication: IEEE Transactions on Neural Networks
Volume: 3
Issue: 5
ISSN: 1045-9227
Subject Category
Cybernetics
Accession Number
93A31268
Distribution Limits
Public
Copyright
Other

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