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Analysis of Abnormal Intra-QRS Potentials in Signal-Averaged Electrocardiograms Using a Radial Basis Function Neural Network
2016
Sensors
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In this manner, a strong, slow varying, normal QRS complex is synthesized by an RBF neural network, and an approximation error is regarded as the weak, rapidly varying AIQPs. ...
Short of clinical trials, a great amount of neurons are employed to build an RBF neural network, and AIQPs are modeled as white noise [17] . ...
RBF Neural Network for the AIQP Analysis As described by Haykin [18] , an RBF neural network refers to a network built with multiple RBFs using a curve fitting approach. ...
doi:10.3390/s16101580
pmid:27690025
pmcid:PMC5087369
fatcat:oizxa3nnrjh3xpsprhx3myulrm
Grain Moisture Sensor Data Fusion Based on Improved Radial Basis Function Neural Network
[chapter]
2013
IFIP Advances in Information and Communication Technology
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The data confusion method of Radial Basis Function (RBF) nerve network is adopted. With improved orthogonal optimal method, the RBF nerve network's weight factors can be obtained. ...
Tests showed that the improved RBF network algorithm reduces the network structure, greatly enhances the learning speed of calculation. ...
RBF Network has a character called optimal approximation, which will avoid local optimum trap during network establishing. ...
doi:10.1007/978-3-642-36137-1_13
fatcat:kmc3jvnswzbbtkpfqsvcazzpnu
Sensitivity analysis applied to the construction of radial basis function networks
2005
Neural Networks
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Hence, sensitivity analysis is expected to be a new alternative way to the construction of RBF networks. q ...
Conventionally, a radial basis function (RBF) network is constructed by obtaining cluster centers of basis function by maximum likelihood learning. ...
Introduction As one of the most popular neural network models, radial basis function (RBF) network attracts lots of attentions on the improvement of its approximate ability as well as the construction ...
doi:10.1016/j.neunet.2005.02.006
pmid:15939573
fatcat:lo7covrbojeqnat5c7zzcoj3oy
Kernel orthonormalization in radial basis function neural networks
1997
IEEE Transactions on Neural Networks
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This paper deals with optimization of the computations involved in training radial basis function (RBF) neural networks. ...
The paper presents a detailed derivation of the proposed network weights calculation procedure and demonstrates its validity for RBF network training on a number of data classification and function approximation ...
space called RBF [6] and an universal approximation scheme popularly known as artificial neural networks (ANN's) [1] . ...
doi:10.1109/72.623218
pmid:18255719
fatcat:3lub6t7wuzhohg6q4vaqde7orm
Comparison Between Beta Wavelets Neural Networks, Rbf Neural Networks And Polynomial Approximation For 1D, 2Dfunctions Approximation
2008
Zenodo
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This paper proposes a comparison between wavelet neural networks (WNN), RBF neural network and polynomial approximation in term of 1-D and 2-D functions approximation. ...
We present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D functions approximation. ...
RBF neural network and polynomial approximation. ...
doi:10.5281/zenodo.1056513
fatcat:bxceneois5bqtbrnyjbdbunpb4
Improving the generalization performance of RBF neural networks using a linear regression technique
2009
Expert systems with applications
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In this paper we present a method for improving the generalization performance of a radial basis function (RBF) neural network. ...
The method uses a statistical linear regression technique which is based on the orthogonal least squares (OLS) algorithm. ...
RBF neural networks are a powerful technique for generating multivariate nonlinear mapping (Bishop, 1991) . An RBF network approximates an unknown mapping function such as f : R n ! R m . ...
doi:10.1016/j.eswa.2009.03.012
fatcat:5v72qrljtzcsxfnzepugtwxygi
Non-destructive testing of layered structures using generalised radial basis function networks trained by the orthogonal least squares learning algorithm
1998
Compel
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The inversion approach is based on the implementation of generalised radial basis function neural networks. ...
The choice of the size of the network and the evaluation of its weights are handled by the orthogonal least squares learning algorithm. ...
These networks offer an alternative to the multilayer perceptron neural networks. ...
doi:10.1108/03321649810203314
fatcat:ayqvzexzyrdj5nokcxuybvvzom
An RBF Neural Network Combined with OLS Algorithm and Genetic Algorithm for Short-Term Wind Power Forecasting
2013
Journal of Applied Mathematics
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This paper proposes a hybrid method that combines orthogonal least squares (OLS) algorithm and genetic algorithm (GA) to construct the radial basis function (RBF) neural network for short-term wind power ...
The OLS algorithm is used to determine the optimal number of nodes in a hidden layer of RBF neural network. ...
RBF neural network is able to provide universal approximation, and in the hidden layer of RBF neural network, basis functions are utilized. ...
doi:10.1155/2013/971389
fatcat:6peugid6ezcgnim3x4skbev5tu
Page 115 of Neural Computation Vol. 8, Issue 1
[page]
1996
Neural Computation
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Advantages of the VMBF over standard planar Radial Basis Functions (RBFs) are discussed.
1 Introduction
Artificial neural networks and approximation techniques typically have been applied to problems conforming ...
The VMBF neural network is used to solve a particular spherical problem of approximating acoustic parameters used to model percep- tual auditory space. ...
Research on Prediction Method of Mechanical Properties of Aluminum Profiles Based on RBF Neural Network
2019
DEStech Transactions on Engineering and Technology Research
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The direct mapping relationship between process parameter subspace and product performance parameter space is established by using RBF neural network. ...
neural network. ...
Because the Radial-Basis Function (RBF) network is a local approximation network, any continuous function can be approximated with arbitrary precision theoretically. ...
doi:10.12783/dtetr/icaen201/29051
fatcat:lovptclxgfdhziw4mbcxli7b4a
Radial basis function neural networks: a topical state-of-the-art survey
2016
Open Computer Science
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This paper aims to offer a compendious and sensible survey on RBF networks. ...
The overall algorithmic development of RBF networks by giving special focus on their learning methods, novel kernels, and fine tuning of kernel parameters have been discussed. ...
OLS-RBF network uses an orthogonal least squares learning algorithm to select suitable centers for the RBF, which makes the training pro-
Name Description P R-by-Q matrix of Q input vectors T S-by-Q ...
doi:10.1515/comp-2016-0005
fatcat:wm2ik77fi5ca7hbq66ssrnssr4
Development of a neural network based algorithm for radar snowfall estimation
1998
IEEE Transactions on Geoscience and Remote Sensing
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The motivation for using a multilayer feedforward neural network (MFNN), such as the radial-basis function (RBF) network, is the good universal function approximation capability of the network. ...
The snowfall estimates from the RBF network are shown to be better than those obtained from conventional Z-S algorithms. ...
The theoretical basis of this technique is the universal approximation theorem, which states that a multilayer feedforward neural network (MFNN), such as the radial-basis function (RBF) or perceptron neural ...
doi:10.1109/36.673664
fatcat:bynrg7ojxjhmdhvu46qli63tly
Environment-adaptation mobile radio propagation prediction using radial basis function neural networks
1997
IEEE Transactions on Vehicular Technology
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The applications to Okumura's data are included to demonstrate the effectiveness of the RBF neural network approach. ...
The RBF neural network is a two-layer localized receptive field network whose output nodes from a combination of radial activation functions computed by the hidden layer nodes. ...
This verifies the effectiveness of the best approximation capability of the RBF neural network. Fig. 1 . 1 Schematic diagram of RBF neural network. ...
doi:10.1109/25.554747
fatcat:y6l4xk6j7zdpffderebs2dg5ay
Approximate B-Spline Surface Based on RBF Neural Networks
[chapter]
2005
Lecture Notes in Computer Science
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According to the strong points of RBF network such as robust, rehabilitating ability and approximating ability to any nonlinear function in arbitrary precision, we presented a new method to reconstruct ...
B-spline surface by using RBF. ...
The prefitting to archetypal surface is achieved by RBF neural network.
The Transform to B-Spline Surface These are the most widely used class of approximating splines. ...
doi:10.1007/11428831_124
fatcat:zmyoe2dj5bhwvhbec2cxqjvy2y
On Global–Local Artificial Neural Networks for Function Approximation
2006
IEEE Transactions on Neural Networks
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We present a hybrid radial basis function (RBF) sigmoid neural network with a three-step training algorithm that utilizes both global search and gradient descent training. ...
Our global-local artificial neural network (GL-ANN) is also seen to compare favorably with both perceptron radial basis net and regression tree derived RBFs. ...
On Global-Local Artificial Neural Networks for Function Approximation David Wedge, David Ingram, David McLean, Clive Mingham, and Zuhair Bandar Abstract-We present a hybrid radial basis function (RBF) ...
doi:10.1109/tnn.2006.875972
pmid:16856657
fatcat:eb5jxar4rvgdboopdmwm6upr7q
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