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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| Vydané v: | Neurocomputing (Amsterdam) Ročník 111; s. 169 - 176 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Amsterdam
Elsevier B.V
02.07.2013
Elsevier |
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | 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 RBF neural networks. A novel Particle Swarm Optimization (PSO) has been applied in the proposed method to optimize the Optimum Steepest Decent (OSD) algorithm. The OSD algorithm could be used in applications where need real-time capabilities for retraining neural networks. To initialize the RBF units more accurately, the new approach based on PSO has been developed and compared with a Conventional PSO clustering algorithm. The obtained results have shown better and same network response in fewer train iterations which is essential for fast retraining of the network. The PSO–OSD and Three-phased OSD algorithms have been applied on five benchmark problems and the results have been compared. Finally, employing the proposed method in a real-time problem has shown interesting outcomes as have come out in this paper. |
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| AbstractList | 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 RBF neural networks. A novel Particle Swarm Optimization (PSO) has been applied in the proposed method to optimize the Optimum Steepest Decent (OSD) algorithm. The OSD algorithm could be used in applications where need real-time capabilities for retraining neural networks. To initialize the RBF units more accurately, the new approach based on PSO has been developed and compared with a Conventional PSO clustering algorithm. The obtained results have shown better and same network response in fewer train iterations which is essential for fast retraining of the network. The PSO–OSD and Three-phased OSD algorithms have been applied on five benchmark problems and the results have been compared. Finally, employing the proposed method in a real-time problem has shown interesting outcomes as have come out in this paper. |
| Author | Montazer, Gholam Ali Fathi, Vahid |
| Author_xml | – sequence: 1 givenname: Vahid surname: Fathi fullname: Fathi, Vahid email: v.fathi@modares.ac.ir – sequence: 2 givenname: Gholam Ali surname: Montazer fullname: Montazer, Gholam Ali email: montazer@modares.ac.ir |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | An improvement in RBF learning algorithm based on PSO for real time applications |
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