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A New Algorithm for Developing Dynamic Radial Basis Function Neural Network Models Based on Genetic Algorithms by Haralambos Sarimveis; Alex Alexandridis; Stefanos Mazarakis; George Bafas is a Engineering article available to read on EtoBox.

What is A New Algorithm for Developing Dynamic Radial Basis Function Neural Network Models Based on Genetic Algorithms about?

A new method for extracting valuable process information from input Á/output data is presented in this paper. The proposed methodology produces dynamical radial basis function (RBF) neural network models based on a specially designed genetic algorithm (GA), which is used to auto-configure the structure of the networks and obtain the model parameters. The new RBF network training technique formulates a complete optimization problem, which includes the network structure into the set of free variables that are used to minimize the prediction error. This is a different approach compared with the local search methods employed by other structure selection mechanisms, which are often trapped to local minima. Another advantage of the proposed method is that only one run of the algorithm is required to obtain the optimal network structure, in contrast to the standard RBF training techniques, where the produced model is selected by trial and error. The effectiveness of the method is illustrated through the development of dynamical models for two sets of data: simulated data from a Continuous Stirred Tank Reactor (CSTR) and true data collected from a Kamyr digester, which is a rather complica

Who reads A New Algorithm for Developing Dynamic Radial Basis Function Neural Network Models Based on Genetic Algorithms?

Author

Haralambos Sarimveis; Alex Alexandridis; Stefanos Mazarakis; George Bafas

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0098-1354)

Published

2004

Language

EN

Field

Engineering (Physical Sciences)