Qasem, Sultan Noman and Shamsuddin, Siti Mariyam (2009) Hybrid Learning Enhancement of RBF network based on particle swarm optimization. In: Advances in Neural Networks – ISNN 2009. Springer Berlin Heidelberg, pp. 19-29. ISBN 3642015123; 978-364201512-0
Full text not available from this repository.
Official URL: http://dx.doi.org/10.1007/978-3-642-01513-7_3
This study proposes RBF Network hybrid learning with Particle Swarm Optimization for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections of weights between the hidden layer and the output layer. The incorporation of PSO in RBF Network hybrid learning is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation on dataset illustrate the effectiveness of PSO in enhancing RBF Network learning.
|Item Type:||Book Section|
|Additional Information:||6th International Symposium on Neural Networks, ISNN 2009; Wuhan; 26 May 2009 through 29 May 2009|
|Uncontrolled Keywords:||hybrid learning, particle swarm optimization, RBF network|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System (Formerly known)|
|Deposited By:||Ms Zalinda Shuratman|
|Deposited On:||30 Sep 2011 15:12|
|Last Modified:||30 Sep 2011 15:12|
Repository Staff Only: item control page