An improvement in RBF learning algorithm based on PSO for real time applications

Radial basis function (RBF) neural networks have been broadly used for classification and interpolation regression. So the idea for trying to develop new learning algorithms for getting better performance of RBF neural networks is an interesting subject. This paper presents a new learning method for...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 111; S. 169 - 176
Hauptverfasser:	Fathi, Vahid, Montazer, Gholam Ali
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 02.07.2013 Elsevier
Schlagworte:	Algorithmics. Computability. Computer arithmetics Applied sciences Artificial intelligence Computer science; control theory; systems Connectionism. Neural networks Exact sciences and technology Neural network Optimum Steepest Descent (OSD) Particle Swarm Optimization (PSO) PSO–OSD Radial basis function (RBF) Theoretical computing Three-phase OSD Optimum Steepest Descent (OSD) Three-phase OSD PSO–OSD Radial basis function (RBF) Neural network Particle Swarm Optimization (PSO) Capability index Steepest descent method Cluster Real time Particle swarm optimization Radial basis function Classification Swarm intelligence PSO-OSD Learning algorithm Artificial intelligence
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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