The Supervised Network Self-Organizing Map for Classification of Large Data Sets
Applied Intelligence
Self-organizing radial basis function network modeling for robot manipulator
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Output value-based initialization for radial basis function neural networks
Neural Processing Letters
Increasing classification efficiency with multiple mirror classifiers
Expert Systems with Applications: An International Journal
Clustering: A neural network approach
Neural Networks
Applying multiobjective RBFNNs optimization and feature selection to a mineral reduction problem
Expert Systems with Applications: An International Journal
A novel BP neural network model for traffic prediction of next generation network
ICNC'09 Proceedings of the 5th international conference on Natural computation
A fuzzy clustering neural networks for motion equations of synchro-drive robot
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A novel RBF neural network with fast training and accurate generalization
CIS'04 Proceedings of the First international conference on Computational and Information Science
New technique for initialization of centres in TSK clustering-based fuzzy systems
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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The key point in design of radial basis function networks is to specify the number and the locations of the centers. Several heuristic hybrid learning methods, which apply a clustering algorithm for locating the centers and subsequently a linear least-squares method for the linear weights, have been previously suggested. These hybrid methods can be put into two groups, which will be called as input clustering (IC) and input-output clustering (IOC), depending on whether the output vector is also involved in the clustering process. The idea of concatenating the output vector to the input vector in the clustering process has independently been proposed by several papers in the literature although none of them presented a theoretical analysis on such procedures, but rather demonstrated their effectiveness in several applications. The main contribution of this paper is to present an approach for investigating the relationship between clustering process on input-output training samples and the mean squared output error in the context of a radial basis function network (RBFN). We may summarize our investigations in that matter as follows: (1) A weighted mean squared input-output quantization error, which is to be minimized by IOC, yields an upper bound to the mean squared output error. (2) This upper bound and consequently the output error can be made arbitrarily small (zero in the limit case) by decreasing the quantization error which can be accomplished through increasing the number of hidden units