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http://dx.doi.org/10.5370/JEET.2008.3.1.108

Design of Polynomial Neural Network Classifier for Pattern Classification with Two Classes  

Park, Byoung-Jun (The Technology Research Institute, GM TECH CO., LTD.)
Oh, Sung-Kwun (Dept. of Electrical Engineering, University of Suwon)
Kim, Hyun-Ki (Dept. of Electrical Engineering, University of Suwon)
Publication Information
Journal of Electrical Engineering and Technology / v.3, no.1, 2008 , pp. 108-114 More about this Journal
Abstract
Polynomial networks have been known to have excellent properties as classifiers and universal approximators to the optimal Bayes classifier. In this paper, the use of polynomial neural networks is proposed for efficient implementation of the polynomial-based classifiers. The polynomial neural network is a trainable device consisting of some rules and three processes. The three processes are assumption, effect, and fuzzy inference. The assumption process is driven by fuzzy c-means and the effect processes deals with a polynomial function. A learning algorithm for the polynomial neural network is developed and its performance is compared with that of previous studies.
Keywords
polynomial networks; pattern classification; spiral; two classes;
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