Non-Linear System Identification Using Lumped Parameter Models With Embedded Feedforward Neural Networks

Fan, Yimin; Li, C. James

doi:10.1006/mssp.2001.1466

Cited by 9 publications

(6 citation statements)

References 10 publications

(12 reference statements)

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“…If the a priori informat ion is insufficient, and the physical model does not reflect the essence of processes, then apply the concept of "black bo x» [13,14] and methods of parametric identification.…”

confidence: 99%

Structural Identification of Dynamic Systems with Hysteresis

Karabutov¹

2016

IJISA

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Abstract-The method of structural identificat ion dynamic systems with a hysteresis in the conditions of uncertainty is developed. The method is based on selection of the special set containing the information on properties of a nonlinear part system. The virtual structure (VS) wh ich allo ws the make the decision about hysteresis structure is offered. The concept of structural identifiability of nonlinear dynamic systems is introduced. Structural identifiability is a necessary condition of obtaining the original form of hysteresis. The criterion of structural identifiability is proposed. The solution of a problem selection the class of the functions belonging to hysteresis to nonlin earities is given.The procedure of structural identification of hysteresis functions is developed. Procedure realization is based on the phenomenological analysis of structure VS. Defin ition of features and properties of the VS is the goal of phenomenological analysis. Each non-linearity introduces the features in the behavior of the system. Therefore, their detection gives only the concrete analysis of VS.Algorith ms of estimation structural parameters the hysteresis in the conditions of uncertainty are offered. They analyze the data in special structural space and are based on the application of secant method VS. Such approach gives adequate estimations of parameters hysteresis. The method of the structurally -frequency analysis is offered for check of the obtained results and estimations. It is based on the analysis of frag ments VS in t wo planes. Such analysis allows the ma ke a decision about hysteresis structure. We show that the offered methodology is applicable to unstable dynamic systems. Results of the computer simulation are given.Index Terms-Structural identificat ion, structure, secant, framework, coefficient of structural properties system, structurally-frequency method hysteresis.

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confidence: 99%

Structural Identification of Dynamic Systems with Hysteresis

Karabutov¹

2016

IJISA

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“…[36] . Characterization may also be partly bypassed by resorting to mathematical functionals capable of representing a vast class of nonlinearities, such as high-order polynomials [44] , neural networks [45] or splines [37] . In this study, we assume the knowledge of nonlinear functional form associated with the thin beam, namely a cubic restoring force in displacement.…”

Section: Selection Of the Nonlinear Basis Functionsmentioning

confidence: 99%

Experimental Modal Analysis of Nonlinear Structures Using Broadband Data

Noël

Renson

Grappasonni

et al. 2016

Nonlinear Dynamics, Volume 1

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The objective of the present paper is to develop a rigorous identification methodology of nonlinear normal modes (NNMs) of engineering structures. This is achieved by processing experimental measurements collected under broadband forcing. The use of such a type of forcing signal allows to excite multiple NNMs simultaneously and, in turn, to save testing time. A two-step methodology integrating nonlinear system identification and numerical continuation of periodic solutions is proposed for the extraction of the individual NNMs from broadband input and output data. It is demonstrated using a numerical cantilever beam possessing a cubic nonlinearity at its free end. The proposed methodology can be viewed as a nonlinear generalization of the phase separation techniques routinely utilized for experimental modal analysis of linear structures. The paper ends with a comparison between this new nonlinear phase separation technique and a previously-developed nonlinear phase resonance method.

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“…We offer the following algorithm of choice structure function () y  on the class ov F . (12), and parameter p will select from a condition (13).…”

Section: Estimation Of Class Nonlinearitymentioning

confidence: 99%

“…The role of the a priori information is noted at structure choice (a nonlinearity form) noted. If the a priori information has not enough or the physical model does not reflect essence of processes it is possible to apply the concept of "a blackbox" [13,14] and methods of parametric identification.…”

Section: Introductionmentioning

confidence: 99%

Structural Identification of Nonlinear Dynamic Systems

Karabutov¹

2015

IJISA

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The method of structural identification nonlinear dynamic systems is offered in the conditions of uncertainty. The method of construction the set containing the data about a nonlinear part of system is developed. The concept of identifiability system for a solution of a problem structural identification is introduced. The special class of structures S for a solution of problem identification is introduced. We will show that the system is identified, if the structure S is closed. The method of estimation the class of nonlinear functions on the basis of the analysis sector sets for the offered structure S is described. We showed, as on S a preliminary conclusion about a form of nonlinear function to make. We offer algorithms of structural identification of single-valued and many-valued nonlinearities. Examples of structural identification of nonlinear systems are considered.

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Non-Linear System Identification Using Lumped Parameter Models With Embedded Feedforward Neural Networks

Cited by 9 publications

References 10 publications

Structural Identification of Dynamic Systems with Hysteresis

Structural Identification of Dynamic Systems with Hysteresis

Experimental Modal Analysis of Nonlinear Structures Using Broadband Data

Structural Identification of Nonlinear Dynamic Systems

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