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Analysis of Abnormal Intra-QRS Potentials in Signal-Averaged Electrocardiograms Using a Radial Basis Function Neural Network

Chun-Cheng Lin
2016 Sensors  
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]

Liu Yang, Gang Wu, Yuyao Song, Lanlan Dong
2013 IFIP Advances in Information and Communication Technology  
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

D. Shi, D.S. Yeung, J. Gao
2005 Neural Networks  
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

W. Kaminski, P. Strumillo
1997 IEEE Transactions on Neural Networks  
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

Wajdi Bellil, Chokri Ben Amar, Adel M. Alimi
2008 Zenodo  
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

C.L. Lin, J.F. Wang, C.Y. Chen, C.W. Chen, C.W. Yen
2009 Expert systems with applications  
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

I.T. Rekanos, T.V. Yioultsis, T.D. Tsiboukis
1998 Compel  
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

Wen-Yeau Chang
2013 Journal of Applied Mathematics  
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  
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

Bangsheng Xing, Le Xu
2019 DEStech Transactions on Engineering and Technology Research  
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

Ch. Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri, Sung-Bae Cho
2016 Open Computer Science  
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

R. Xiao, V. Chandrasekar, Hongping Liu
1998 IEEE Transactions on Geoscience and Remote Sensing  
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

Po-Rong Chang, Wen-Hao Yang
1997 IEEE Transactions on Vehicular Technology  
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]

Xumin Liu, Houkuan Huang, Weixiang Xu
2005 Lecture Notes in Computer Science  
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

D. Wedge, D. Ingram, D. Mclean, C. Mingham, Z. Bandar
2006 IEEE Transactions on Neural Networks  
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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