Fuzzy pattern classification using feedforward neural networks with multilevel hidden neurons

This paper introduces feedforward neural networks inherently capable of fuzzy classification of non-sparse or overlapping pattern classes. These networks are unique in that the hidden layers consist of multilevel neurons. The multilevel hidden neurons allow the networks to learn the fuzziness in the input data and also to minimize the within-class variances. The performance of the proposed networks over an overlapping pattern set is compared with that of conventional feedforward networks trained for crisp classification and those trained for fuzzy classification. The results show that the proposed networks reduce misclassification errors and have considerably better generalization ability.<
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