Results 191 to 200 of about 14,713 (239)
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Adaptive RBF neural network in signal detection
Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94, 2002This paper addresses the application of locally optimum (LO) signal detection techniques to environments in which the noise density is not known a-priori. For small signal levels, the LO detection rule is shown to involve a nonlinearity which depends on the noise density.
Wahid Ahmed +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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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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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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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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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 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 ...
Sheng Chen, Xia Hong
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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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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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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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