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

- Journal title : Journal of Electrical Engineering and Technology
- Volume 3, Issue 1, 2008, pp.108-114
- Publisher : The Korean Institute of Electrical Engineers
- DOI : 10.5370/JEET.2008.3.1.108

Title & Authors

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

Park, Byoung-Jun; Oh, Sung-Kwun; Kim, Hyun-Ki;

Park, Byoung-Jun; Oh, Sung-Kwun; Kim, Hyun-Ki;

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;

Language

English

Cited by

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