Combining Regression Trees and Radial Basis Function Networks in Longitudinal Data Modelling

2003 ◽  
pp. 83-91
Author(s):  
Marilena Pillati ◽  
Daniela G. Calò ◽  
Giuliano Galimberti
Keyword(s):  
Longitudinal Data ◽  
Radial Basis Function ◽  
Basis Function ◽  
Regression Trees ◽  
Radial Basis Function Networks ◽  
Data Modelling ◽  
Radial Basis

COMBINING REGRESSION TREES AND RADIAL BASIS FUNCTION NETWORKS

2000 ◽  
Vol 10 (06) ◽  
pp. 453-465 ◽  
Author(s):  
MARK ORR ◽  
JOHN HALLAM ◽  
KUNIO TAKEZAWA ◽  
ALAN MURRAY ◽  
SEISHI NINOMIYA ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Regression Trees ◽  
Radial Basis Function Networks ◽  
Data Sets ◽  
Parametric Regression ◽  
Real World Problem ◽  
Radial Basis ◽  
Soybean Plants

We describe a method for non-parametric regression which combines regression trees with radial basis function networks. The method is similar to that of Kubat,1 who was first to suggest such a combination, but has some significant improvements. We demonstrate the features of the new method, compare its performance with other methods on DELVE data sets and apply it to a real world problem involving the classification of soybean plants from digital images.

Engine Test Data Modelling by Evolutionary Radial Basis Function Networks

2003 ◽  
Vol 217 (6) ◽  
pp. 489-497 ◽  
Author(s):  
W Lin ◽  
M H Wu ◽  
S Duan
Keyword(s):  
Radial Basis Function ◽  
Test Data ◽  
Basis Function ◽  
Radial Basis Function Networks ◽  
Engine Test ◽  
Data Modelling ◽  
Test Bed ◽  
Real Coded Genetic Algorithm ◽  
Radial Basis ◽  
Modelling Task

The engine test bed is introduced briefly and the importance of modelling for the engine test is discussed. The application of combining radial basis function (RBF) networks and a real-coded genetic algorithm (RCGA) to create the model is described for the engine test. Finally, the experimental results are analysed and it is shown that the proposed approach combining RCGA and RBF models is well suited for the engine test data modelling task.

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.

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