2002
DOI: 10.1007/978-1-4471-0107-9
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Identification and Control Using Volterra Models

Abstract: Identification and control using Volterra models. -( Communications and control engineering) l.Volterra equations 2.Automatic control-Mathematical models I.Title II.

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Cited by 209 publications
(196 citation statements)
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“…In order to build a model of Volterra [8] oculo-motor system (OMS) a person is encouraged to use the test deterministic effects, for example, step signals (the most appropriate for the study of the dynamics of OMS) [9], which simplifies the computational algorithm to identify and significantly reduce the time of processing of experimental data. There is a method and computer algorithms identifying deterministic nonlinear dynamical systems in the form of Volterra models using multi-test signals [10].…”
Section: Statement Of the Problemmentioning
confidence: 99%
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“…In order to build a model of Volterra [8] oculo-motor system (OMS) a person is encouraged to use the test deterministic effects, for example, step signals (the most appropriate for the study of the dynamics of OMS) [9], which simplifies the computational algorithm to identify and significantly reduce the time of processing of experimental data. There is a method and computer algorithms identifying deterministic nonlinear dynamical systems in the form of Volterra models using multi-test signals [10].…”
Section: Statement Of the Problemmentioning
confidence: 99%
“…To describe the objects of unknown structure appropriate to use the most universal nonlinear nonparametric dynamic models -Volterra model [8]. The nonlinear and dynamic properties investigated object is uniquely described by a sequence of invariant with respect to the type of input signal is of multidimensional weight functions -Volterra kernels.…”
Section: The Volterra Model and Identification Omsmentioning
confidence: 99%
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“…Following the work of Doyle et al [16] of using 50% of samples for model identification and 50% for model verification, the first 50% of the dataset (21,409 pairs) is used to train the RSFTDNN model, while the remaining 50% of the dataset (21,409 pairs) is used for testing. As a criterion, the normalized root mean square error (NRMSE) is used to evaluate the performance and accuracy of the model:…”
Section: Experiments Setupmentioning
confidence: 99%
“…It appears in text books [26,10] and has been used for demonstration of control schemes and fault diagnosis. The reaction equations are non-linear because they include the bilinear products of flow rates, composition and temperature as well as the temperature dependence of reaction rate [26].…”
Section: First Principles Modelsmentioning
confidence: 99%