Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)
- 2005.05a
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- Pages.6-8
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- 2005
Optimal Identification of Nonlinear Process Data Using GAs-based Fuzzy Polynomial Neural Networks
유전자 알고리즘 기반 퍼지 다항식 뉴럴네트워크를 이용한 비선형 공정데이터의 최적 동정
- Published : 2005.05.14
Abstract
In this paper, we discuss model identification of nonlinear data using GAs-based Fuzzy Polynomial Neural Networks(GAs-FPNN). Fuzzy Polynomial Neural Networks(FPNN) is proposed model based Group Method Data Handling(GMDH) and Neural Networks(NNs). Each node of FPNN is expressed Fuzzy Polynomial Neuron(FPN). Network structure of nonlinear data is created using Genetic Algorithms(GAs) of optimal search method. Accordingly, GAs-FPNN have more inflexible than the existing models (in)from structure selecting. The proposed model select and identify its for optimal search of Genetic Algorithms that are no. of input variables, input variable numbers and consequence structures. The GAs-FPNN model is select tuning to input variable number, number of input variable and the last part structure through optimal search of Genetic Algorithms. It is shown that nonlinear data model design using Genetic Algorithms based FPNN is more usefulness and effectiveness than the existing models.
Keywords
- Genetic Algorithms(GAs);
- Fuzzy Polynomial Neural Networks(FPNN);
- Fuzzy Polynomial Neuron(FPN);
- Grout Method of Data Handling(GMDH);
- Linear Data