Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)
- 2006.04a
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- Pages.270-272
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- 2006
Design of Genetic Algorithms-based Fuzzy Polynomial Neural Networks Using Symbolic Encoding
기호 코딩을 이용한 유전자 알고리즘 기반 퍼지 다항식 뉴럴네트워크의 설계
- Published : 2006.04.29
Abstract
In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs) using symbolic coding for non-linear data. One of the major subject of genetic algorithms is representation of chromosomes. The proposed model optimized by the means genetic algorithms which used symbolic code to represent chromosomes. The proposed gFPNN used a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.
Keywords
- Symbolic Encoding;
- Fuzzy Polynomial Neural Networks(FPNN);
- Genetic Algorithms(GAs);
- Membership Functions(MFs);
- Optimal Model