Ant colony optimization of clustering models: Research Articles
International Journal of Intelligent Systems
Network Anomaly Detection Based on DSOM and ACO Clustering
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
IEEE Transactions on Neural Networks
Neuron selection for RBF neural network classifier based on data structure preserving criterion
IEEE Transactions on Neural Networks
Deep web sources classifier based on DSOM-EACO clustering model
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Distributed denial of service attack detection using an ensemble of neural classifier
Computer Communications
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The central problem in training a radial basis function neural network (RBFNN) is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose an enhanced swarm intelligence clustering (ESIC) method to select hidden layer neurons, and then, training a cosine RBFNN base on gradient descent learning process. Also, the new method is applied for intrusion detection. Experimental results show that the average DR and FPR of our ESIC-based RBFNN detection classifier maintained a better performance than BP, SVM and OLS RBF.