Identification for Critical Flutter Load and Boundary Conditions of a Beam Using Neural Networks

Takahashi, Ichiro

doi:10.1006/jsvi.1999.2451

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…Figures 5,6,7,8,9,10,11 and 12 show the comparison of the measured horizontal and vertical displacements in three directions with the predicted ones. …”

Section: Continuous Evolutionary Algorithm and Its Improvementsmentioning

confidence: 95%

“…Cao developed an approach to the identification of the loads acting on aircraft wings, which used an artificial neural network to model the load-strain relationship in structural analysis [7]. Takahashi studied the possibility of using a multilayer perception network furnished with the backpropagation algorithm to detect the critical flutter load and the boundary conditions of tapered beam [8]. In order to identify and predict the unsteady transonic aerodynamic loads, Marques applied the genetic algorithm to optimization of the network architecture and used random search algorithm to update weight and bias values [9].…”

Section: Introductionmentioning

confidence: 99%

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Identification of vibration loads on hydro generator by using hybrid genetic algorithm

Liu

2006

Acta Mech Sin

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Vibration dynamic characteristics have been a major issue in the modeling and mechanical analysis of large hydro generators. An algorithm is developed for identifying vibration dynamic characteristics by means of hybrid genetic algorithm. From the measured dynamic responses of a hydro generator, an appropriate estimation algorithm is needed to identify the loading parameters, including the main frequencies and amplitudes of vibrating forces. In order to identify parameters in an efficient and robust manner, an optimization method is proposed that combines genetic algorithm with simulated annealing and elitist strategy. The hybrid genetic algorithm is then used to tackle an ill-posed problem of parameter identification, in which the effectiveness of the proposed optimization method is confirmed by its comparison with actual observation data.

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“…Figures 5,6,7,8,9,10,11 and 12 show the comparison of the measured horizontal and vertical displacements in three directions with the predicted ones. …”

Section: Continuous Evolutionary Algorithm and Its Improvementsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Identification of vibration loads on hydro generator by using hybrid genetic algorithm

Liu

2006

Acta Mech Sin

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“…Many applications of feed-forward neural networks to material engineering are presented in reference [8]. In reference [9], the identi"cation of #utter and boundary conditions of a cantilever beam is performed using feed-forward networks.…”

Section: Introductionmentioning

confidence: 99%

Neural Identification of Non-Linear Dynamic Structures

Riche¹,

Gualandris²,

Thomas³

et al. 2001

Journal of Sound and Vibration

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“…Narendra and Parthasarathy (1990) have presented a comprehensive study on the applicability of multilayer neural networks for identification and subsequent use to control non-linear dynamic systems. Takahashi (1999) has presented a multilayer neural network trained by using the backpropagation algorithm to detect the critical aerodynamic loading for the occurrence of flutter and the limit conditions in the structure. Maghami et al (2000) have presented a new procedure for developing and training artificial neural networks, useful for fast and efficient control as well as for the analysis of flexible space systems.…”

Section: Introductionmentioning

confidence: 99%

Application of time-delay neural and recurrent neural networks for the identification of a hingeless helicopter blade flapping and torsion motions

Marques¹,

Souza²,

Rebolho³

et al. 2005

J. Braz. Soc. Mech. Sci. & Eng.

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System identification consists of the development of techniques for model estimation from experimental data, demanding no previous knowledge of the process. Aeroelastic models are directly influence of the benefits of identification techniques, basically because of the difficulties related to the modelling of the coupled aero- and structural dynamics. In this work a comparative study of the bilinear dynamic identification of a helicopter blade aeroelastic response is carried out using artificial neural networks is presented. Two neural networks architectures are considered in this study. Both are variations of static networks prepared to accomodate the system dynamics. A time delay neural networks (TDNN) for response prediction and a typical recurrent neural networks (RNN) are used for the identification. The neural networks have been trained by Levemberg-Marquardt algorithm. To compare the performance of the neural networks models, generalization tests are produced where the aeroelastic responses of the blade in flapping and torsion motions at its tip due to noisy pitching angle are presented. An analysis in frequency of the signals from simulated and the emulated models are presented. In order to perform a qualitative analysis, return maps with the simulation results generated by the neural networks are presented

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Identification for Critical Flutter Load and Boundary Conditions of a Beam Using Neural Networks

Cited by 8 publications

References 14 publications

Identification of vibration loads on hydro generator by using hybrid genetic algorithm

Identification of vibration loads on hydro generator by using hybrid genetic algorithm

Neural Identification of Non-Linear Dynamic Structures

Application of time-delay neural and recurrent neural networks for the identification of a hingeless helicopter blade flapping and torsion motions

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