An adaptive PID neural network for complex nonlinear system control
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092746" target="_blank" >RIV/61989100:27240/14:86092746 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/14:86092746
Result on the web
<a href="http://dx.doi.org/10.1016/j.neucom.2013.03.065" target="_blank" >http://dx.doi.org/10.1016/j.neucom.2013.03.065</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neucom.2013.03.065" target="_blank" >10.1016/j.neucom.2013.03.065</a>
Alternative languages
Result language
angličtina
Original language name
An adaptive PID neural network for complex nonlinear system control
Original language description
Usually it is difficult to solve the control problem of a complex nonlinear system. In this paper, we present an effective control method based on adaptive PID neural network and particle swarm optimization (PSO) algorithm. PSO algorithm is introduced toinitialize the neural network for improving the convergent speed and preventing weights trapping into local optima. To adapt the initially uncertain and varying parameters in the control system, we introduce an improved gradient descent method to adjustthe network parameters. The stability of our controller is analyzed according to the Lyapunov method. The simulation of complex nonlinear multiple-input and multiple-output (MIMO) system is presented with strong coupling. Empirical results illustrate that the proposed controller can obtain good precision with shorter time compared with the other considered methods.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neurocomputing
ISSN
0925-2312
e-ISSN
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Volume of the periodical
135
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
7
Pages from-to
79-85
UT code for WoS article
000335871200010
EID of the result in the Scopus database
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