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http://dx.doi.org/10.5370/KIEE.2014.63.4.534

The Design of Pattern Classification based on Fuzzy Combined Polynomial Neural Network  

Rho, Seok-Beom (Dept. of Electronics Convergence Engineering, Wonkwang University)
Jang, Kyung-Won (Dept. of Electronics Convergence Engineering, Wonkwang University)
Ahn, Tae-Chon (Dept. of Electronics Convergence Engineering, Wonkwang University)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.63, no.4, 2014 , pp. 534-540 More about this Journal
Abstract
In this paper, we propose a fuzzy combined Polynomial Neural Network(PNN) for pattern classification. The fuzzy combined PNN comes from the generic TSK fuzzy model with several linear polynomial as the consequent part and is the expanded version of the fuzzy model. The proposed pattern classifier has the polynomial neural networks as the consequent part, instead of the general linear polynomial. PNNs are implemented by stacking the simple polynomials dynamically. To implement one layer of PNNs, the various types of simple polynomials are used so that PNNs have flexibility and versatility. Although the structural complexity of the implemented PNNs is high, the PNNs become a high order-multi input polynomial finally. To estimate the coefficients of a polynomial neuron, The weighted linear discriminant analysis. The output of fuzzy rule system with PNNs as the consequent part is the linear combination of the output of several PNNs. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets.
Keywords
Polynomial neural networks; Weighted linear discriminant analysis; Polynomial neuron; Fuzzy combined polynomial neural networks; Discriminant function;
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