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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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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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A New RBF Neural Network With Boundary Value Constraints [PDF]
IEEE Transactions on Systems, Man, and Cybernetics, 2009 We present a novel topology of the radial basis function (RBF) neural network, referred to as the boundary value constraints (BVC)-RBF, which is able to automatically satisfy a set of BVC. Unlike most existing neural networks whereby the model is identified via learning from observational data only, the proposed BVC-RBF offers a generic framework by ...
Xia Hong, Xinlei Chen
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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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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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An efficient learning method for RBF Neural Networks
2015 International Joint Conference on Neural Networks (IJCNN), 2015Radial Basis Functions Neural Network (RBFNN) as the outcome of recent research provides a simple model for complex networks. This is achieved by employing the Radial Basis Function (RBF) in the network as hidden neuron patterns. The optimal properties of the RBFs pave the way for stable approximation.
Maryam Pazouki +3 more
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The Application of Different RBF Neural Network in Approximation
2011The value algorithms of classical function approximation theory have a common drawback: the compute-intensive, poor adaptability, high model and data demanding and the limitation in practical applications. Neural network can calculate the complex relationship between input and output, therefore, neural network has a strong function approximation ...
Jincai Chang, Long Zhao, Qianli Yang
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Adaptive control on manifolds with RBF neural networks
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002We propose a new method of adaptive control on manifolds for nonlinear plants in the full-state feedback case using radial basis function (RBF) neural networks. We introduce a procedure for synthesis of adaptation algorithms based on associated performance criteria.
Valeri A. Terekhov +2 more
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Earthquake Prediction by RBF Neural Network Ensemble
2004Earthquake Prediction is one of the most difficult subjects in the world. It is difficult to simulate the non-linear relationship between the magnitude of earthquake and many complicated attributes arising the earthquake. In this paper, RBF neural network ensemble was employed to predict the magnitude of earthquake.
Yue Liu +4 more
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Research on a RBF Neural Network in Stereo Matching
2011There are so many shortcomings in current stereo matching algorithms, for example, they have a low robustness, so as to be influenced by the environment easily, especially the intensity of the light and the number of the occlusion areas; also they often have a poor practical performance for they are difficult to deal with the matching problem without ...
Sheng Xu 0003 +4 more
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