Neural Network Based Bicriterial Dual Control with Multiple Linearization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F10%3A43898286" target="_blank" >RIV/49777513:23520/10:43898286 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3182/20100826-3-TR-4015.00051" target="_blank" >http://dx.doi.org/10.3182/20100826-3-TR-4015.00051</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3182/20100826-3-TR-4015.00051" target="_blank" >10.3182/20100826-3-TR-4015.00051</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural Network Based Bicriterial Dual Control with Multiple Linearization

Popis výsledku v původním jazyce

A suboptimal dual controller for discrete nonlinear stochastic systems based on the bicriterial approach is proposed and discussed. Two individual criteria are designed and used to introduce one of the conflicting efforts between estimation and control;caution and probing. A nonlinear system is modeled using a neural network (NN) of perceptron type. The unknown parameters of the network are estimated by a global estimation method, the Gaussian sum method (GSM), which allows to determine conditional probability density function (pdf) of the NNs parameters. The GSM in association with an idea of multiple linearization is chosen and utilized in the bicriterial dual control (BDC) approach. The probing component of the control law is determined for each local mode of estimated pdf separately and respects accuracy of each local estimate inherent in the estimated pdf. A comparison of the proposed modified BDC and the BDC which uses a global point estimate only is shown in a numerical example

Název v anglickém jazyce

Neural Network Based Bicriterial Dual Control with Multiple Linearization

Popis výsledku anglicky

A suboptimal dual controller for discrete nonlinear stochastic systems based on the bicriterial approach is proposed and discussed. Two individual criteria are designed and used to introduce one of the conflicting efforts between estimation and control;caution and probing. A nonlinear system is modeled using a neural network (NN) of perceptron type. The unknown parameters of the network are estimated by a global estimation method, the Gaussian sum method (GSM), which allows to determine conditional probability density function (pdf) of the NNs parameters. The GSM in association with an idea of multiple linearization is chosen and utilized in the bicriterial dual control (BDC) approach. The probing component of the control law is determined for each local mode of estimated pdf separately and respects accuracy of each local estimate inherent in the estimated pdf. A comparison of the proposed modified BDC and the BDC which uses a global point estimate only is shown in a numerical example

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název periodika

IFAC-PapersOnline

ISSN

1474-6670

e-ISSN

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Svazek periodika

10

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

TR - Turecká republika

Počet stran výsledku

6

Strana od-do

1-6

Kód UT WoS článku

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EID výsledku v databázi Scopus

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