A neuro-fuzzy architecture for high performance classification
Identifieur interne : 002D18 ( Main/Merge ); précédent : 002D17; suivant : 002D19A neuro-fuzzy architecture for high performance classification
Auteurs : Sung-Bae Cho [Corée du Sud]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 1995.
Abstract
Abstract: The concept of combining modular neural networks has been recently exploited as a new direction for the development of highly reliable neural network systems in the area of pattern classification. In this paper we present an efficient method for combining the modular networks based on fuzzy logic, especially the fuzzy integral. This method nonlinearly combines objective evidences, in the form of network outputs, with subjective evaluation of the reliability of the individual neural networks. Also, for more effective aggregation, we adopt the extension of the fuzzy integral with ordered weighted averaging operators. The experimental results with the recognition problem of on-line handwriting characters show that the performance of individual networks could be improved significantly.
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DOI: 10.1007/3-540-60607-6_6
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