Global Optimization for Neural Network Training
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Kernel-Based Nonparametric Regression Method
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
The COMPSET algorithm for subset selection
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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
Cluster-based instance selection for machine classification
Knowledge and Information Systems
Improvement of neural network classifier using floating centroids
Knowledge and Information Systems
Least squares quantization in PCM
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
Accelerating FCM neural network classifier using graphics processing units with CUDA
Applied Intelligence
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In this paper the agent-based population learning algorithm designed to train RBF networks (RBFN's) is proposed. The algorithm is used to network initialization and estimation of its output weights. The approach is based on the assumption that a location of the radial based function centroids can be modified during the training process. It is shown that such a floating centroids may help to find the optimal neural network structure. In the proposed implementation of the agent-based population learning algorithm, RBFN initialization and RBFN training based on the floating centroids are carried-out by a team of agents, which execute various local search procedures and cooperate to find-out a solution to the considered RBFN training problem. Two variants of the approach are suggested in the paper. The approaches are implemented and experimentally evaluated.