대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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- Pages.152-154
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- 2005
유전론적 최적 퍼지 다항식 뉴럴네트워크와 다변수 소프트웨어 공정으로의 응용
Genetically Optimized Fuzzy Polynomial Neural Networks and Its Application to Multi-variable Software Process
- 발행 : 2005.10.28
초록
In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed genetic algorithms-based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.
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