Training RBF neural networks with PSO and improved subtractive clustering algorithms
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Training RBF neural network via quantum-behaved particle swarm optimization
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Evolving RBF neural networks for pattern classification
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Training RBF neural network with hybrid particle swarm optimization
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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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.