Results 201 to 210 of about 13,817 (251)
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2001
Determining the parameters of a Radial Basis Function Neural Nerwork (number of neurons, and their respective centers and radii) is often done by hand, or based in methods highly dependent on initial values. In this work, Evolutionary Algorithms are used to automatically build a RBF NN that solves a specified problem.
Víctor Manuel Rivas Santos +2 more
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Determining the parameters of a Radial Basis Function Neural Nerwork (number of neurons, and their respective centers and radii) is often done by hand, or based in methods highly dependent on initial values. In this work, Evolutionary Algorithms are used to automatically build a RBF NN that solves a specified problem.
Víctor Manuel Rivas Santos +2 more
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RBF neural network controller based on OLSSVR
2013 9th Asian Control Conference (ASCC), 2013In this paper, a predictive adaptation method based on Online Least Square Support Vector Regression (OLSVR) for a RBF controller has been proposed. System Jacobian is approximated via Online LSSVR model of the system to tune RBF controller. The parameters of the controller have been tuned depending on K-step ahead future behavior of the system to ...
Kemal Ucak, Gülay Öke Günel
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Orthogonal RBF Neural Network Approximation
Neural Processing Letters, 1999The approximation properties of the RBF neural networks are investigated in this paper. A new approach is proposed, which is based on approximations with orthogonal combinations of functions. An orthogonalization framework is presented for the Gaussian basis functions. It is shown how to use this framework to design efficient neural networks.
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Ml-rbf: RBF Neural Networks for Multi-Label Learning
Neural Processing Letters, 2009Multi-label learning deals with the problem where each instance is associated with multiple labels simultaneously. The task of this learning paradigm is to predict the label set for each unseen instance, through analyzing training instances with known label sets.
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A Modified RBF Neural Network in Pattern Recognition
2007 International Joint Conference on Neural Networks, 2007This paper presents a modified radial basis function (RBF) neural network for pattern recognition problems, which uses a hybrid learning algorithm to adaptively adjust the structure of the network. Two strategies are used to attain the compromise between the network complexity and accuracy, one is a modified "novelty" condition to create a new neuron ...
Min Han 0001, Wei Guo, Yunfeng Mu
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A Modified RBF Neural Network for Network Anomaly Detection
2006A modified RBF (radial basis function)-based neural network is proposed for network anomaly detection. Special attention is given to the determination of the parameters of the hidden layer. We propose a novel grid-based approach to compress and cluster the training data.
Xiaotao Wei +2 more
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RBF neural networks for handwriting process modelling
2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2011Handwriting process is one of the most complex processes of our biological repertory. Modelling of such process remains difficult to implement. Several approaches were proposed in the literature. However, the validation results of these models remain less or more satisfactory and the basic models were the subject of improvement in the objective to ...
Mohamed Aymen Slim +2 more
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The Application of RBF Neural Network in Earthquake Prediction
2009 Third International Conference on Genetic and Evolutionary Computing, 2009RBF (Radial Basis Function) neural network is used to predict the magnitude of earthquake in this article. The self-adaptive and nonlinear approach abilities of RBF neural network are suitable to process the complexity of the production mechanism of earthquake.
Ying Wang, Yi Chen, Jinkui Zhang
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The RBF Neural Network in Approximate Dynamic Programming
1999A radial basis function (RBF) neural network was applied to an optimal control problem. The role of an approximation architecture in the task of dynamic programming is emphasised. While it has been proved that dynamic programming works well for moderate discrete spaces, research is continuing on how to apply dynamic programming techniques to large ...
Branko Ster, Andrej Dobnikar
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A simple hierarchical approximation RBF neural network
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., 2006The approximation algorithm introduced by Asim Roy et al. (1997) generates a hybrid neural network with RBF neurons and other types of hidden neurons for function approximation. The network is trained in stages, with RBF neurons at the early stages corresponding to general features in the space and those in later stages corresponding to more specific ...
Peggy Israel Doerschuk +1 more
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