Paper Info
Title
Neuro-genetic approach to multidimensional fuzzy reasoning for pattern classification
Abstract
To tackle the pattern classification problems first we give a new interpretation to the multidimensional fuzzy implication (MFI). This new interpretation of MFI is used for multidimensional fuzzy reasoning (MFR) for pattern classification. We realize the new interpretation through multilayer perceptron. The learning scheme of the network is based on genetic algorithm (GA), A weight smoothing scheme is also proposed to improve neural network's generalization capability. The smoothing constraint is incorporated into the objective function of the network to reflect the neighborhood correlation and to seek those solutions which have smooth connection weights. At the learning stage of the neural network fuzzy linguistic statements have been used. Once learned, the nonfuzzy features of a pattern can be classified using a fuzzy masking. The performance of the proposed scheme is tested through synthetic data. Finally, we apply the proposed scheme to the vowel recognition problem of one Indian language. (C) 2000 Elsevier Science B.V. All rights reserved.
Year
DOI
Venue
2000
10.1016/S0165-0114(98)00013-X
Fuzzy Sets and Systems
Keywords
DocType
Volume
multidimensional fuzzy implication,multidimensional fuzzy reasoning,pattern classification,multilayer perceptron,genetic algorithm,regularization
Journal
112
Issue
ISSN
Citations 
3
0165-0114
11
PageRank 
References 
Authors
0.72
5
2
Name
Order
Citations
PageRank
Kumar S. Ray134949.30
Jayati Ghoshal2251.90