Title :
Radial Basis Function Networks Applied to Process Control
Author :
Hofland, A.G. ; Morris, A.J. ; Montague, G.A.
Author_Institution :
Department of Chemical and Process Engineering, University of Newcastle, Newcastle-upon-Tyne, NE1 7RU, U.K.
Abstract :
There are strong relationships between radial basis function (RBF) approaches and neural network representations. Indeed, the RBF representation can be implementated in the form of a two-layered network. This paper examines the contribution that RBF networks can make to the process modelling and control toolbox. Radial basis function networks are compared with sigmoidal activation function feedforward networks using data from a large industrial process.
Keywords :
Approximation methods; Artificial neural networks; Content addressable storage; Network topology; Neurons; Parameter estimation; Process control; Radial basis function networks; System identification; Tellurium;
Conference_Titel :
American Control Conference, 1992
Conference_Location :
Chicago, IL, USA
Print_ISBN :
0-7803-0210-9
