JACIII Vol.10 No.1 pp. 35-49
doi: 10.20965/jaciii.2006.p0035


Genetically Optimized Multi-Layer Fuzzy Polynomial Neural Networks: Analysis and Design

Sung-Kwun Oh*, Witold Pedrycz**,***, and Ho-Sung Park****

*Department of Electrical Engineering, The University of Suwon, San 2-2, Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do 445-743, South Korea

**Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G6, Canada

***Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

****Department of Electrical Electronic and Information Engineering, Wonkwang University, 344-2, Shinyong-Dong, Iksan, Chon-Buk, 570-749, South Korea

November 22, 2004
June 20, 2005
January 20, 2006
genetically optimized Fuzzy Polynomial Neural Networks (gFPNN), Fuzzy Polynomial Neuron, Multi-Layer Perceptron, Genetic Algorithms, Group Method of Data Handling, design procedure
In this study, we introduce a new category of neurofuzzy networks – Fuzzy Polynomial Neural Networks and develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms, in particular. The augmented genetically optimized FPNN (referred to as gFPNN) is a structurally optimized architecture which comes with a higher level of flexibility in comparison to the one we have encountered in the conventional FPNN. The GA-based design procedure being applied to each layer of FPNN leads to the selection of preferred nodes (or FPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas for the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation where we use a number of modeling benchmarks – synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.
Cite this article as:
S. Oh, W. Pedrycz, and H. Park, “Genetically Optimized Multi-Layer Fuzzy Polynomial Neural Networks: Analysis and Design,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.1, pp. 35-49, 2006.
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