The modeling of non-linear systems using fast radial basis functoin

Author

Fathalla, Joma M.

Author_Institution

Gen. Public Comm. of Electr., Water & Gas

fYear

2009

fDate

23-26 March 2009

Firstpage

1

Lastpage

5

Abstract

The radial basis function (RBF) is designed and implemented for modelling a nonlinear system. The combination of k-means clustering algorithm, p-nearest neighbour, least mean square together with the Gaussian function are used for determining the optimal network parameters adaptively instead of trial and error. In this paper an algorithm is presented to design (RBF) with smallest possible number of centres, proper width, and an optimal network output. An example of modelling a nonlinear system is simulated and the satisfactory results are obtained and illustrated.

Keywords

Gaussian processes; least mean squares methods; nonlinear systems; parameter estimation; pattern clustering; radial basis function networks; Gaussian function; fast radial basis function; k-means clustering algorithm; least mean square; nonlinear system; optimal network parameter; p-nearest neighbour; Adaptive algorithm; Algorithm design and analysis; Artificial neural networks; Clustering algorithms; Multi-layer neural network; Neural networks; Nonlinear systems; Partitioning algorithms; Radial basis function networks; Signal design; K-means clustering; Non-linear system; RBFnetwork; adaptive algorithm; proper parameters;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Signals and Devices, 2009. SSD '09. 6th International Multi-Conference on

Conference_Location

Djerba

Print_ISBN

978-1-4244-4345-1

Electronic_ISBN

978-1-4244-4346-8

Type

conf

DOI

10.1109/SSD.2009.4956724

Filename

4956724