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| Title: | A comparison of PSO and backpropagation for training RBF neural networks for identification of a power system with STATCOM | |
| Author (s): | Mohaghegi, S. del Valle, Y. Venayagamoorthy, Ganesh K. Harley, R.G. | |
| Department/Lab Affiliations: | Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory | |
| Keywords: | PSO RBF neural network training STATCOM Static Compensator backpropagation backpropagation algorithm particle swarm optimization power system control power system identification radial basis function networks radial basis function neural network | |
| Issue Date: | 2005 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Mohaghegi, S.; del Valle, Y.; Venayagamoorthy, G.K.; Harley, R.G., "A comparison of PSO and backpropagation for training RBF neural networks for identification of a power system with STATCOM" SIS 2005. Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. pp. 381- 384, 8-10 June 2005 | |
| Abstract: | Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a radial basis function neural network (RBFN) is compared with that of particle swarm optimization, for neural network based identification of a small power system with a static compensator. The comparison of the two methods is based on the convergence speed and robustness of each method. | |
| Type: | Article - Conference proceedings text | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
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| title | A comparison of PSO and backpropagation for training RBF neural networks for identification of a power system with STATCOM | |
| contributor.author | Mohaghegi, S. | |
| contributor.author | del Valle, Y. | |
| contributor.author | Venayagamoorthy, Ganesh K. | |
| contributor.author | Harley, R.G. | |
| contributor.deptlab | Electrical and Computer Engineering | |
| contributor.deptlab | Real-Time Power and Intelligent Systems Laboratory | |
| subject | PSO | |
| subject | RBF neural network training | |
| subject | STATCOM | |
| subject | Static Compensator | |
| subject | backpropagation | |
| subject | backpropagation algorithm | |
| subject | particle swarm optimization | |
| subject | power system control | |
| subject | power system identification | |
| subject | radial basis function networks | |
| subject | radial basis function neural network | |
| date.issued | 2005 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Mohaghegi, S.; del Valle, Y.; Venayagamoorthy, G.K.; Harley, R.G., "A comparison of PSO and backpropagation for training RBF neural networks for identification of a power system with STATCOM" SIS 2005. Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. pp. 381- 384, 8-10 June 2005 | |
| identifier.pub.URI | ||
| description.abstract | Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a radial basis function neural network (RBFN) is compared with that of particle swarm optimization, for neural network based identification of a small power system with a static compensator. The comparison of the two methods is based on the convergence speed and robustness of each method. | |
| type | Article - Conference proceedings | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| date.accessioned | 2007-04-05T14:24:04Z | |
| date.available | 2007-04-05T14:24:04Z | |
| identifier.persist.URI | ||
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