KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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GA-based Feed-forward Self-organizing Neural Network Architecture and Its Applications for Multi-variable Nonlinear Process Systems

  • Oh, Sung-Kwun (Department of Electrical Engineering, University of Suwon) ;
  • Park, Ho-Sung (Department of Electrical Engineering, University of Suwon) ;
  • Jeong, Chang-Won (Department of Computer Engineering, Wonkwang University) ;
  • Joo, Su-Chong (Department of Computer Engineering, Wonkwang University)
  • Published : 2009.06.25
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Abstract

In this paper, we introduce the architecture of Genetic Algorithm(GA) based Feed-forward Polynomial Neural Networks(PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes(PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System(MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

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