Results 201 to 210 of about 14,713 (239)
Some of the next articles are maybe not open access.
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
openaire +1 more source
Encrypting Algorithm Based on RBF Neural Network
Third International Conference on Natural Computation (ICNC 2007), 2007An 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 +3 more
openaire +2 more sources
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
openaire +1 more source
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
openaire +2 more sources
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
openaire +1 more source
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
openaire +2 more sources
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
openaire +1 more source
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
openaire +3 more sources
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.
openaire +3 more sources
RBF Neural Networks and Descartes’ Rule of Signs
2002We establish versions of Descartes' rule of signs for radial basis function (RBF) neural networks. These RBF rules of signs provide tight bounds for the number of zeros of univariate networks with certain parameter restrictions. Moreover, they can be used to derive tight bounds for the Vapnik-Chervonenkis (VC) dimension and pseudo-dimension of these ...
openaire +2 more sources