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Advanced Self-Organizing Neural Networks Based on Competitive Fuzzy Polynomial Neurons  

박호성 (원광대학 제어계측공학과)
박건준 (원광대학 전기전자공학부)
이동윤 (중부대학 정보통신공학과)
오성권 (대한전기학회)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers D / v.53, no.3, 2004 , pp. 135-144 More about this Journal
Abstract
In this paper, we propose competitive fuzzy polynomial neurons-based advanced Self-Organizing Neural Networks(SONN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. The proposed SONN dwells on the ideas of fuzzy rule-based computing and neural networks. And it consists of layers with activation nodes based on fuzzy inference rules and regression polynomial. Each activation node is presented as Fuzzy Polynomial Neuron(FPN) which includes either the simplified or regression polynomial fuzzy inference rules. As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership (unction are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SONN architectures, that is, the basic and modified one with both the generic and the advanced type. Here the basic and modified architecture depend on the number of input variables and the order of polynomial in each layer. The number of the layers and the nodes in each layer of the SONN are not predetermined, unlike in the case of the popular multi-layer perceptron structure, but these are generated in a dynamic way. The superiority and effectiveness of the Proposed SONN architecture is demonstrated through two representative numerical examples.
Keywords
SONN(Self-Organizing Neural Networks); activation node; FPN(Fuzzy Polynomial Neuron); regression polynomial fuzzy inference; the basic and the modified type; the generic and the advanced type;
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Times Cited By KSCI : 1  (Citation Analysis)
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