GA-based Feed-forward Self-organizing Neural Network Architecture and Its Applications for Multi-variable Nonlinear Process Systems
Sung‐Kwun Oh · KSII Transactions on Internet and Information Systems · 2009
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.