Authors:
Gancho Vachkov
1
;
Nikolinka Christova
2
and
Magdalena Valova
2
Affiliations:
1
The University of the South Pacific (USP), Fiji
;
2
University of Chemical Technology and Metallurgy, Bulgaria
Keyword(s):
Radial Basis Function Networks, RBF Models, Parameter Tuning, Optimization Strategies, Particle Swarm Optimization, Supervised Learning.
Related
Ontology
Subjects/Areas/Topics:
Computer Simulation Techniques
;
Formal Methods
;
Neural Nets and Fuzzy Systems
;
Optimization Issues
;
Simulation and Modeling
;
Simulation Tools and Platforms
Abstract:
In this paper the problem of tuning the parameters of the RBF networks by using optimization methods is investigated. Two modifications of the classical RBFN, called Reduced and Simplified RBFN are introduced and analysed in the paper. They have a smaller number of parameters. Three optimization strategies that perform one or two steps for tuning the parameters of the RBFN models are explained and investigated in the paper. They use the particle swarm optimization algorithm with constraints. The one-step Strategy 3 is a simultaneous optimization of all three groups of parameters, namely the Centers, Widths and the Weights of the RBFN. This strategy is used in the paper for performance evaluation of the Reduced and Simplified RBFN models. A test 2-dimensional example with high nonlinearity is used to create different RBFN models with different number of RBFs. It is shown that the Simplified RBFN models can achieve almost the same modelling accuracy as the Reduced RBFN models. This mak
es the Simplified RBFN models a preferable choice as a structure of the RBFN model.
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