[논문]Genetically Optimized Hybrid Fuzzy Neural Networks Based on Linear Fuzzy Inference Rules

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

Genetically Optimized Hybrid Fuzzy Neural Networks Based on Linear Fuzzy Inference Rules 원문보기

International Journal of Control, Automation and Systems, v.3 no.2, 2005년, pp.183 - 194

Oh Sung-Kwun (Department of Electrical Engineering, The University of Suwon) , Park Byoung-Jun (Department of Electrical Electronic & Information Engineering, Wonkwang University) , Kim Hyun-Ki (Department of Electrical Engineering, The University of Suwon)

Abstract ▼ AI-Helper

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

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