scholarly journals Radial basis function neural networks: a topical state-of-the-art survey

2016 ◽  
Vol 6 (1) ◽  
pp. 33-63 ◽  
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Ajit Kumar Behera ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Research Work ◽  
Fine Tuning ◽  
Special Focus ◽  
Global Approximation ◽  
Rbf Networks ◽  
Fast Training ◽  
Radial Basis ◽  
Emergent Behaviors

AbstractRadial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors. This paper aims to offer a compendious and sensible survey on RBF networks. The advantages they offer, such as fast training and global approximation capability with local responses, are attracting many researchers to use them in diversified fields. 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. In addition, we have considered the recent research work on optimization of multi-criterions in RBF networks and a range of indicative application areas along with some open source RBFN tools.

Universal Approximation Using Radial-Basis-Function Networks

1991 ◽  
Vol 3 (2) ◽  
pp. 246-257 ◽  
Author(s):  
J. Park ◽  
I. W. Sandberg
Keyword(s):  
Finite Number ◽  
Radial Basis Function ◽  
Basis Function ◽  
Universal Approximation ◽  
Radial Basis Function Networks ◽  
Feedforward Networks ◽  
Rbf Networks ◽  
Smoothing Factor ◽  
Radial Basis ◽  
Hidden Layer

There have been several recent studies concerning feedforward networks and the problem of approximating arbitrary functionals of a finite number of real variables. Some of these studies deal with cases in which the hidden-layer nonlinearity is not a sigmoid. This was motivated by successful applications of feedforward networks with nonsigmoidal hidden-layer units. This paper reports on a related study of radial-basis-function (RBF) networks, and it is proved that RBF networks having one hidden layer are capable of universal approximation. Here the emphasis is on the case of typical RBF networks, and the results show that a certain class of RBF networks with the same smoothing factor in each kernel node is broad enough for universal approximation.

Intelligent Parallel Networks for Combustion Quality Monitoring in Power Station Boilers

2013 ◽  
Vol 699 ◽  
pp. 893-899 ◽  
Author(s):  
K. Sujatha ◽  
N. Pappa ◽  
U. Siddharth Nambi ◽  
C.R. Raja Dinakaran ◽  
K. Senthil Kumar
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Research Work ◽  
Back Propagation ◽  
Average Intensity ◽  
Back Propagation Algorithm ◽  
Feature Size ◽  
Propagation Algorithm ◽  
Radial Basis

This research work includes a combination of Fisher’s Linear Discriminant (FLD) analysis by combining Radial Basis Function Network (RBF) and Back Propagation Algorithm (BPA) for monitoring the combustion conditions of a coal fired boiler so as to control the air/fuel ratio. For this two dimensional flame images are required which was captured with CCD camera whose features of the images, average intensity, area, brightness and orientation etc., of the flame are extracted after pre-processing the images. The FLD is applied to reduce the n-dimensional feature size to 2 dimensional feature size for faster learning of the RBF. Also three classes of images corresponding to different burning conditions of the flames have been extracted from a continuous video processing. In this the corresponding temperatures, the Carbon monoxide (CO) emissions and other flue gases have been obtained through measurement. Further the training and testing of Parallel architecture of Radial Basis Function and Back Propagation Algorithm (PRBFBPA) with the data collected have been done and the performance of the algorithms is presented.

ASSESSING RBF NETWORKS USING DELVE

2000 ◽  
Vol 10 (05) ◽  
pp. 397-415 ◽  
Author(s):  
MARK ORR ◽  
JOHN HALLAM ◽  
ALAN MURRAY ◽  
TOM LEONARD
Keyword(s):  
Machine Learning ◽  
Radial Basis Function ◽  
Basis Function ◽  
The Other ◽  
Rbf Networks ◽  
Radial Basis ◽  
Regression Problems ◽  
Using Data

In this paper, different methods for training radial basis function (RBF) networks for regression problems are described and illustrated. Then, using data from the DELVE archive, they are empirically compared with each other and with some other well known methods for machine learning. Each of the RBF methods performs well on at least one DELVE task, but none are as consistent as the best of the other non-RBF methods.

scholarly journals Sample Data Synchronization and Harmonic Analysis Algorithm Based on Radial Basis Function Interpolation

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Huaiqing Zhang ◽  
Yu Chen ◽  
Zhihong Fu ◽  
Ran Liu
Keyword(s):  
Radial Basis Function ◽  
Harmonic Analysis ◽  
Fourth Order ◽  
Basis Function ◽  
Harmful Effect ◽  
Approximation Schemes ◽  
Global Approximation ◽  
Zero Crossing ◽  
Sampling Sequence ◽  
Radial Basis

The spectral leakage has a harmful effect on the accuracy of harmonic analysis for asynchronous sampling. This paper proposed a time quasi-synchronous sampling algorithm which is based on radial basis function (RBF) interpolation. Firstly, a fundamental period is evaluated by a zero-crossing technique with fourth-order Newton’s interpolation, and then, the sampling sequence is reproduced by the RBF interpolation. Finally, the harmonic parameters can be calculated by FFT on the synchronization of sampling data. Simulation results showed that the proposed algorithm has high accuracy in measuring distorted and noisy signals. Compared to the local approximation schemes as linear, quadric, and fourth-order Newton interpolations, the RBF is a global approximation method which can acquire more accurate results while the time-consuming is about the same as Newton’s.

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