Identification of Nonlinear Dynamic Systems Using Neural Networks

Masri, Sami F.; Chassiakos, Anastasios; Caughey, T. K.

doi:10.1115/1.2900734

Cited by 186 publications

(66 citation statements)

References 6 publications

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“…Despite the many developments, nonparametric identification schemes can still be computationally expensive, depending on the type and scale of the system. As an example of the use of a ANN based scheme, we cite the work of Masri, Chassiakos, and Caughey (231) . They use a three-layered feed-forward neural network to model a damped Düffing oscillator.…”

Section: Journal Of System Design and Dynamicsmentioning

confidence: 99%

A Review of Nonlinear Dynamics of Mechanical Systems in Year 2008

Shaw

Balachandran

2008

JSDD

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In this article, we provide an overview of a selection of topics of current interest in nonlinear dynamics and vibrations of mechanical systems. Specifically, we cover the traditional topics of structural dynamics, rotating systems, vibration control, vehicle dynamics, and machining, as well as some topics that have emerged more recently, namely micro-and nano-electromechanical systems, piecewise smooth systems, and structural health monitoring and system identification. In the introductory and closing sections, we offer our perspective on the state of affairs in nonlinear dynamics and its utility in the study of mechanical systems.

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Section: Journal Of System Design and Dynamicsmentioning

confidence: 99%

A Review of Nonlinear Dynamics of Mechanical Systems in Year 2008

Shaw

Balachandran

2008

JSDD

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“…These limitations make it di cult for non-parametric methods to be applied in on-line applications. Among the nonparametric identiÿcation schemes, the artiÿcial neural network approach has been recently used in characterizing structure-unknown non-linear systems [11,12].…”

Section: Introductionmentioning

confidence: 99%

On‐line identification and damage detection in non‐linear structural systems using a variable forgetting factor approach

Lin

Betti

2003

Earthq Engng Struct Dyn

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SUMMARYThis paper presents an adaptive on-line identiÿcation algorithm based on a newly deÿned variable forgetting factor approach. At each time step, the proposed methodology, a recursive least-square (Kalman ÿlter) based algorithm, upgrades the adaptation gain matrix using an adaptive forgetting factor that is expressed as the ratio between the minimum value of the diagonal elements of the adaptation gain matrix and a set of pre-deÿned threshold values. This approach requires only acceleration measurements and is particularly robust to the integration errors introduced in determining velocities and displacements. Such an algorithm has been implemented for both parametric and non-parametric identiÿcation studies. For the case of no a priori information on the type of the structural model, an analytical method based on a power series of multivariable polynomial expansions has been introduced. The e ectiveness and robustness of the proposed approach has been shown in various numerical simulations, considering the e ects of excitation amplitude, of measurement noise, of a priori mass estimation error, and of exact-, under-, and over-parameterization of the structural model. General polynomial-type non-linear models such as Du ng and Van der Pole oscillators and Bouc-Wen hysteretic model have been analyzed using the proposed modeling process. The methodology presented in this paper leads to a more accurate and controllable on-line identiÿcation either for the estimation of time-invariant or time-varying structural parameters (for damage detection purposes) or for the prediction of the future structural response to dynamic loading.

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“…In the engineering field, when ships or other floating bodies are subjected to large roll motions in waves, either when sailing or when at anchor, they experience nonlinear roll damping, which can be characterized as a type of velocitydependent damping of the form   f y  in eqn (1). This phenomenon is usually caused physically by the effects of nonlinear free-surface wave making and the nonlinear viscous dissipation of fluid flow.…”

Section: Motion Responsesmentioning

confidence: 99%

“…In the last two decades, there has been a lot of research work on the subject of dynamic system identification conducted by Masri et al [1], Chassiakos and Masri [2], Liang et al [3,4], Spina et al [5], Iourtchenko and Dimentberg [6], and others [7][8][9][10][11][12][13][14][15][16][17]. However, most of this research has identified the nonlinear characteristics of either damping or the restoring force.…”

Section: Introductionmentioning

confidence: 99%

A numerical scheme for recovering the nonlinear characteristics of a single degree of freedom structure: non-parametric system identification

Park¹,

Jang²,

Syngellakis³

et al. 2014

WIT Transactions on the Built Environment

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The aim of this paper is to present a numerical scheme for the identification of the nonlinear characteristics of a dynamically excited, single degree of freedom structure, using a non-parametric procedure, recently proposed by the second author; this involves the simultaneous identification of the nonlinear characteristics of both damping and restoring force in dynamic systems whose damping depends on velocity alone. According to this method, the response of the structure is first measured then an integral equation accounting for its unknown nonlinear characteristics is derived. This is an integral equation of the first kind, involving numerical instability in the Hadamard sense. To overcome this difficulty, the Landweber regularization, combined with the L-curve criterion, is applied to the integral equation. Adopting a dynamic model for a test structure, the corresponding nonlinear system identification is achieved through the proposed numerical solution of the governing integral equation.

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Identification of Nonlinear Dynamic Systems Using Neural Networks

Cited by 186 publications

References 6 publications

A Review of Nonlinear Dynamics of Mechanical Systems in Year 2008

A Review of Nonlinear Dynamics of Mechanical Systems in Year 2008

On‐line identification and damage detection in non‐linear structural systems using a variable forgetting factor approach

A numerical scheme for recovering the nonlinear characteristics of a single degree of freedom structure: non-parametric system identification

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