Neuro-genetic approach to multidimensional fuzzy reasoning for pattern classification

✍ Scribed by Kumar S. Ray; Jayati Ghoshal

• Publisher: Elsevier Science
• Year: 2000
• Tongue: English
• Weight: 788 KB
• Volume: 112
• Category: Article
• ISSN: 0165-0114
• DOI: 10.1016/s0165-0114(98)00013-x

✦ Synopsis

To tackle the pattern classiÿcation problems ÿrst 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 classiÿcation. 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 re ect 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 classiÿed 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.