Algorithms for better representation and faster learning in radial basis function networks
Advances in neural information processing systems 2
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Improving performance of radial basis function network based with particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
IEEE Transactions on Neural Networks
Fast forward RBF network construction based on particle swarm optimization
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
A hybrid particle swarm optimization and its application in neural networks
Expert Systems with Applications: An International Journal
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
RBF neural network based on particle swarm optimization
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
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In this paper we investigate the implementation of particle swarm optimization in the design of radial basis function neural networks under the framework of input-output fuzzy clustering. The problem being studied concerns the optimal estimation of the basis function centers, provided that the learning process is guided by the information of the output space. The proposed method encompasses a cost function, which is defined by a reformulated version of the fuzzy c-means applied in the product (i.e. input-output) space. The minimization of this function is accomplished by using the particle swarm optimization, where each particle encodes a set of cluster centers associated to a single fuzzy partition. The algorithm is simple and easy to implement, yet very effective. The performance of the resulting network is tested and verified through a number of experimental cases in terms of a 10-fold cross validation analysis.