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Multilayer perceptron, fuzzy sets, and classification
Abstract
A fuzzy neural network model based on the multilayer perceptron, using the backpropagation 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 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 other related models.
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Cites
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Pal, S. K., Pramanik, A., Maiti, J., & Mitra, P. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02293-7 · Cited by 298
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Metadata
- Authors
- Pal, S. K., & Mitra, S.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1992
- Volume
- 3
- Issue
- 5
- Pages
- 683-697
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2095727900
- Cited by
- 1,113
- References
- 26
- FWCI
- 15.65
- Type
- journal article
- Language
- en
- Open access
- closed
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