Neural Identification of Non-Linear Dynamic Structures

Riche, Rodolphe Le; Gualandris, David; Thomas, Jean-Hugh; Hemez, François M.

doi:10.1006/jsvi.2001.3737

Cited by 25 publications

(11 citation statements)

References 21 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: 94%

“…Interpretive method II (IMII) identified the forces completely in the modal co-ordinates by using the Euler beam theory and modal analysis technique [5]. System identification and artificial intelligence techniques are more directly geared towards these constraints than traditional modeling for two reasons: they involve simpler representation than the finite element method and directly account for errors when the model is created [6]. System identification offers a probabilistic framework to the representation.…”

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: 94%

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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“…It is capable of learning the complex nonlinear relationships among the data and perform well with missing or incomplete data [11]. The superiority of ANNs is demonstrated in many fields, such as weather forecasting, bankruptcy forecasting, foreign exchange rate forecasting, stock price forecasting, electric load forecasting, car sales forecasting, etc [8].…”

Section: Methodsmentioning

confidence: 99%

A neural network based method for part demands prediction in auto aftermarket

Chen

2010

2010 IEEE International Conference on Software Engineering and Service Sciences

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In the supply chain management of auto aftermarket, companies strive to manage inventory with low cost while maintaining a reasonable order fulfillment rate. To achieve this objective, a critical issue is to predict the demand for auto parts with high accuracy. Based on the factors relevant to auto aftermarket, this paper proposed an artificial neural network based method to forecast the demands of auto parts. The effectiveness of the proposed method is illustrated with a case study of an auto 4s shop in Shanghai.

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“…To achieve the load identification, a neural network model has been implemented to derive the forces from the acceleration measurements. This work is detailed in [9]. More generally, the non parametric models such as neural network allow to identify a lot of information that is not directly recorded : Obstacle recognition, torque in the driveline, weight of the car (the number of passengers and luggage), type of road (highway, city, …).…”

Section: Customer Surveymentioning

confidence: 99%

Structural durability in automotive design

Thomas¹,

Nguyen-Tajan

Burry

2005

Materialwissenschaft Werkst

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The fatigue design is realized at PSA within a probabilistic approach. This approach takes into account the industrial context of the design. It includes the analysis of customer distribution, the handling of the production scatter, and the definition of relevant loading specifications ad acceptance criterion for the designers. This approach avoids variable amplitude analysis by the mean of an appropriate fatigue equivalence method, and by the mean of the Dang Van criterion for the fatigue analysis of the FEM results. Extension of this work to structures undergoing damage due to vibrations is adressed.

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Neural Identification of Non-Linear Dynamic Structures

Cited by 25 publications

References 21 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

A neural network based method for part demands prediction in auto aftermarket

Structural durability in automotive design

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