Results 221 to 230 of about 59,820 (274)

Adaptive Computation Algorithm for RBF Neural Network

IEEE Transactions on Neural Networks and Learning Systems, 2012
A novel learning algorithm is proposed for nonlinear modelling and identification using radial basis function neural networks. The proposed method simplifies neural network training through the use of an adaptive computation algorithm (ACA). In addition, the convergence of the ACA is analyzed by the Lyapunov criterion. The proposed algorithm offers two
Hong-Gui Han, Junfei Qiao 0001
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Encrypting Algorithm Based on RBF Neural Network

Third International Conference on Natural Computation (ICNC 2007), 2007
An encrypting algorithm based on RBF neural network is presented. The clear-text is regarded as RBF target vector. RBF network is trained with random input vector, and the trained results are regarded as the cryptograph. With the feature that the input vector of RBF neural network can be any random value, there are the infinite kinds of ways to build ...
Kaili Zhou, Yaohong Kang, Yan Huang, Erli Feng   +3 more
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Recognition of pump state by RBF neural network

2005 IEEE International Conference on Robotics and Biomimetics - ROBIO, 2005
The arising of neural network theory is a breakthrough from machine processing to man's thinking mode. On the basis of the traditional BP and RBF neural network, this article applies a new algorithm user-defined step radial basis function to recognize ZYB03-60 vacuum air press pump. It turns out that the algorithm can train studying-speed faster and it
Jimei Wu, Qiumin Wu
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Evolving RBF Neural Networks

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, Pedro Ángel Castillo Valdivieso, Juan Julián Merelo Guervós   +2 more
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RBF neural network controller based on OLSSVR

2013 9th Asian Control Conference (ASCC), 2013
In 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, 1999
The 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 in Pattern Recognition

2007 International Joint Conference on Neural Networks, 2007
This 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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Adaptive RBF neural network in signal detection

Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94, 2002
This 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, Donald M. Hummels, Mohamad T. Musavi   +2 more
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Ml-rbf: RBF Neural Networks for Multi-Label Learning

Neural Processing Letters, 2009
Multi-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 for Network Anomaly Detection

2006
A 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, Houkuan Huang, Shengfeng Tian   +2 more
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