Results 211 to 220 of about 13,817 (251)
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, Sheng Chen
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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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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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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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Evolving RBF Neural Networks for Pattern Classification
2005When a radial-basis function neural network (RBFNN) is used for pattern classification, the problem involves designing the topology of RBFNN and also its centers and widths. In this paper, we present a particle swarm optimization (PSO) learning algorithm to automate the design of RBF networks, to solve pattern classification problems.
Zheng Qin +3 more
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A novel Hybrid RBF Neural Networks model as a forecaster
Statistics and Computing, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Oguz Akbilgic +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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RBF Neural Networks and Radial Fuzzy Systems
2015RBF neural networks are an efficient tool for acquisition and representation of functional relations reflected in empirical data. The interpretation of acquired knowledge is, however, generally difficult because the knowledge is encoded into values of the parameters of the network.
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