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    Sequential Prediction-Error Method for Structural Identification

    Source: Journal of Engineering Mechanics:;1997:;Volume ( 123 ):;issue: 002
    Author:
    Chung-Bang Yun
    ,
    Hyeong-Jin Lee
    ,
    Chang-Gun Lee
    DOI: 10.1061/(ASCE)0733-9399(1997)123:2(115)
    Publisher: American Society of Civil Engineers
    Abstract: Time-domain methods for the identification of linear structural dynamic systems are studied. The stochastic autoregressive and moving average (ARMAX) model is used to process the measured excitation and response records contaminated by noises. The study focuses on the sequential prediction-error method incorporating several techniques for improving the parameter estimation. They are the exponential data weighting, the global data weighting, and the square-root estimation techniques. Efficient procedures of the square-root estimation are developed for the multi-input and multioutput (MIMO) case as well as the multi-input and single-output (MISO) case. Verifications of the present methods are carried out using the simulated time histories for the input excitation and output response, as well as using the experimental data on a building model. The results indicate that the square-root estimation technique is particularly effective for improving the convergence and accuracy of the sequential estimation, even with crude initial guesses.
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      Sequential Prediction-Error Method for Structural Identification

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    http://yetl.yabesh.ir/yetl1/handle/yetl/84549
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    contributor authorChung-Bang Yun
    contributor authorHyeong-Jin Lee
    contributor authorChang-Gun Lee
    date accessioned2017-05-08T22:38:13Z
    date available2017-05-08T22:38:13Z
    date copyrightFebruary 1997
    date issued1997
    identifier other%28asce%290733-9399%281997%29123%3A2%28115%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/84549
    description abstractTime-domain methods for the identification of linear structural dynamic systems are studied. The stochastic autoregressive and moving average (ARMAX) model is used to process the measured excitation and response records contaminated by noises. The study focuses on the sequential prediction-error method incorporating several techniques for improving the parameter estimation. They are the exponential data weighting, the global data weighting, and the square-root estimation techniques. Efficient procedures of the square-root estimation are developed for the multi-input and multioutput (MIMO) case as well as the multi-input and single-output (MISO) case. Verifications of the present methods are carried out using the simulated time histories for the input excitation and output response, as well as using the experimental data on a building model. The results indicate that the square-root estimation technique is particularly effective for improving the convergence and accuracy of the sequential estimation, even with crude initial guesses.
    publisherAmerican Society of Civil Engineers
    titleSequential Prediction-Error Method for Structural Identification
    typeJournal Paper
    journal volume123
    journal issue2
    journal titleJournal of Engineering Mechanics
    identifier doi10.1061/(ASCE)0733-9399(1997)123:2(115)
    treeJournal of Engineering Mechanics:;1997:;Volume ( 123 ):;issue: 002
    contenttypeFulltext
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