Artificial Neural Network with Radial Basis Function in Model Predictive Control of Chemical Reactor

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28110%2F09%3A63509302" target="_blank" >RIV/70883521:28110/09:63509302 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Artificial Neural Network with Radial Basis Function in Model Predictive Control of Chemical Reactor

Original language description

This paper describes the application of artificial neural network with radial basis function as a predictor in model predictive control. Radial basis function neural networks are known for their fast training. Thus, this type of artificial neural networks offers promising way how to reduce computational cost during offline predictor training and eventual online adaptation. The features of this type of artificial neural network are presented in simulations in MATLAB/Simulink on the nonlinear system control. The aim of this paper is to suggest one approach how to solve nonlinear prediction problem using artificial neural network respecting computational demands of the predictor..

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JP - Industrial processes and processing

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2009

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Quarterly Mechanincs

ISSN

1734-8927

e-ISSN

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Volume of the periodical

28

Issue of the periodical within the volume

3

Country of publishing house

PL - POLAND

Number of pages

6

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

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