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Optimization RBFNNs Parameters using Genetic Algorithms: Applied on Function Approximation
Mohammed Awad
Pages - 295 - 307     |    Revised - 30-06-2010     |    Published - 10-08-2010
Volume - 4   Issue - 3    |    Publication Date - July 2010  Table of Contents
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KEYWORDS
Radial Basis Function Neural Networks, Genetic Algorithms, Function Approximation.
ABSTRACT
This paper deals with the problem of function approximation from a given set of input/output (I/O) data. The problem consists of analyzing training examples, so that we can predict the output of a model given new inputs. We present a new approach for solving the problem of function approximation of I/O data using Radial Basis Function Neural Networks (RBFNNs) and Genetic Algorithms (GAs). This approach is based on a new efficient method of optimizing RBFNNs parameters using GA, this approach uses GA to optimize centers c and radii r of RBFNNs, such that each individual of the population represents centers and radii of RBFNNs. Singular value decomposition (SVD) is used to optimize weights w of RBFNNs. The GA initial population performed by using Enhanced Clustering Algorithm for Function Approximation (ECFA) to initialize the RBF centers c and k-nearest neighbor to initialize the radii r. The performance of the proposed approach has been evaluated on cases of one and two dimensions. The results show that the function approximation using GA to optimize RBFNNs parameters can achieve better normalized-root- mean square-error than those achieved by traditional algorithms.
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Dr. Mohammed Awad
Arab American University - Palestinian Occupied
m.awad@aauj.edu