MCP Based Noise Resistant Algorithm for Training RBF Networks and Selecting Centers

Implementation of an optimal control strategy for a hydraulic hybrid vehicle using CMAC and RBF networks

Scientia Iranica ◽

10.1016/j.scient.2012.02.019 ◽

2012 ◽

Vol 19 (2) ◽

pp. 327-334 ◽

Cited By ~ 12

Author(s):

A. Taghavipour ◽

M.S. Foumani ◽

M. Boroushaki

Keyword(s):

Optimal Control ◽

Control Strategy ◽

Hybrid Vehicle ◽

Optimal Control Strategy ◽

Rbf Networks ◽

Hydraulic Hybrid ◽

Hydraulic Hybrid Vehicle

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Improving constructive training of RBF networks through selective pruning and model selection

Neurocomputing ◽

10.1016/j.neucom.2004.11.027 ◽

2005 ◽

Vol 64 ◽

pp. 537-541 ◽

Cited By ~ 21

Author(s):

Adriano L.I. Oliveira ◽

Bruno J.M. Melo ◽

Silvio R.L. Meira

Keyword(s):

Model Selection ◽

Rbf Networks

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Improving RBF Networks using Square Root UKF

2007 IEEE International Conference on Control and Automation ◽

10.1109/icca.2007.4376533 ◽

2007 ◽

Author(s):

Dazi Li ◽

Haitao Zhang ◽

Qibing Jin ◽

Yanrui Geng

Keyword(s):

Square Root ◽

Rbf Networks

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Universal Approximation Using Radial-Basis-Function Networks

Neural Computation ◽

10.1162/neco.1991.3.2.246 ◽

1991 ◽

Vol 3 (2) ◽

pp. 246-257 ◽

Cited By ~ 2306

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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