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    Dynamic Recurrent Neural Networks for Control of Unknown Nonlinear Systems

    Source: Journal of Dynamic Systems, Measurement, and Control:;1994:;volume( 116 ):;issue: 004::page 567
    Author:
    Liang Jin
    ,
    Peter N. Nikiforuk
    ,
    Madan M. Gupta
    DOI: 10.1115/1.2899254
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A scheme of dynamic recurrent neural networks (DRNNs) is discussed in this paper, which provides the potential for the learning and control of a general class of unknown discrete-time nonlinear systems which are treated as “black boxes” with multi-inputs and multi-outputs (MIMO). A model of the DRNNs is described by a set of nonlinear difference equations, and a suitable analysis for the input-output dynamics of the model is performed to obtain the inverse dynamics. The ability of a DRNN structure to model arbitrary dynamic nonlinear systems is incorporated to approximate the unknown nonlinear input-output relationship using a dynamic back propagation (DBP) learning algorithm. An equivalent control concept is introduced to develop a model based learning control architecture with simultaneous on-line identification and control for unknown nonlinear plants. The potentials of the proposed methods are demonstrated by simulation results.
    keyword(s): Nonlinear systems , Artificial neural networks , Dynamics (Mechanics) , Algorithms , Equations , Industrial plants AND Simulation results ,
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      Dynamic Recurrent Neural Networks for Control of Unknown Nonlinear Systems

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    http://yetl.yabesh.ir/yetl1/handle/yetl/113293
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    • Journal of Dynamic Systems, Measurement, and Control

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    contributor authorLiang Jin
    contributor authorPeter N. Nikiforuk
    contributor authorMadan M. Gupta
    date accessioned2017-05-08T23:43:41Z
    date available2017-05-08T23:43:41Z
    date copyrightDecember, 1994
    date issued1994
    identifier issn0022-0434
    identifier otherJDSMAA-26211#567_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/113293
    description abstractA scheme of dynamic recurrent neural networks (DRNNs) is discussed in this paper, which provides the potential for the learning and control of a general class of unknown discrete-time nonlinear systems which are treated as “black boxes” with multi-inputs and multi-outputs (MIMO). A model of the DRNNs is described by a set of nonlinear difference equations, and a suitable analysis for the input-output dynamics of the model is performed to obtain the inverse dynamics. The ability of a DRNN structure to model arbitrary dynamic nonlinear systems is incorporated to approximate the unknown nonlinear input-output relationship using a dynamic back propagation (DBP) learning algorithm. An equivalent control concept is introduced to develop a model based learning control architecture with simultaneous on-line identification and control for unknown nonlinear plants. The potentials of the proposed methods are demonstrated by simulation results.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleDynamic Recurrent Neural Networks for Control of Unknown Nonlinear Systems
    typeJournal Paper
    journal volume116
    journal issue4
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.2899254
    journal fristpage567
    journal lastpage576
    identifier eissn1528-9028
    keywordsNonlinear systems
    keywordsArtificial neural networks
    keywordsDynamics (Mechanics)
    keywordsAlgorithms
    keywordsEquations
    keywordsIndustrial plants AND Simulation results
    treeJournal of Dynamic Systems, Measurement, and Control:;1994:;volume( 116 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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