Combining RBF Networks Trained by Different Clustering Techniques
Neural Processing Letters
An Adaptive Learning Algorithm Aimed at Improving RBF Network Generalization Ability
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Improving RBF Networks by the Feature Selection Approach EUBAFES
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Neural Computing and Applications
A possibilistic approach to RBFN centers initialization
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid Learning Enhancement of RBF Network Based on Particle Swarm Optimization
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Improving performance of radial basis function network based with particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Hi-index | 0.00 |
In this paper, Particle Swarm Optimization (PSO) and improved subtractive clustering algorithm were proposed for training RBF neural networks. PSO was used to feature selection in conjunction with RBF classifiers for individual fitness evaluation. During RBF training process, supervised mean subtractive clustering algorithm (SMSCA) was used to evolve RBF networks dynamically with the selected feature subset based on PSO algorithm. Experimental results on four datasets show that RBF networks evolved by our proposed algorithm have more simple architecture and stronger generalization ability with nearly the same classification performance when compared with the networks evolved by other methods.