Parameter extraction and optimization using Levenberg-Marquardt algorithm

Le, Duc-Hung; Cong-Kha, Pham; Trang, Nguyen Thi Thien; Tu, Bui Trong

doi:10.1109/cce.2012.6315945

Cited by 28 publications

(18 citation statements)

References 5 publications

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“…The Levenberg-Marquardt algorithm is an efficient and popular damped least squre technique. This algorithm is a combinaison between the steepest gradient descent and the Gauss-Newton algorithms [42]. The activation function at the output of the HLs is the sigmoid function, it delivers a continuously smoother range of values between 0 and 1 and is less expensive in terms of calculation.…”

Section: Neural Network Trainingmentioning

confidence: 99%

Monitoring of solenoid parameters based on neural networks and optical fiber squeezer for solenoid valves diagnosis

Zahidi

Amrane

Azami

et al. 2021

IJECE

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As crucial parts of various engineering systems, solenoid valves (SVs) operated by electromagnetic solenoid (EMS) are of great importance and their failure may lead to cause unexpected casualties. This failure, characterized by a degradation of the performances of the SVs, could be due to a fluctuations in the EMS parameters. These fluctuations are essentially attributed to the changes in the spring constant, coefficient of friction, inductance, and the resistance of the coil. Preventive maintenance by controlling and monitoring these parameters is necessary to avoid eventual failure of these actuators. The authors propose a new methodology for the functional diagnosis of electromagnetic solenoids (EMS) used in hydraulic systems. The proposed method monitors online the electrical and mechanical parameters varying over time by using artificial neural networks algorithm coupled with an optical fiber polarization squeezer based on EMS for polarization scrambling. First, the MATLAB/Simulink model is proposed to analyze the effect of the parameters on the dynamic EMS model. The result of this simulation is used for training the neural network, then a simulation is proposed using the neural net fitting toolbox to determine the solenoid parameters (Resistance of the coil R, stiffness K and coefficient of friction B of the spring) from the coefficients of the transfer function, established from the model step response. Future work will include not only diagnosing failure modes, but also predicting the remaining life based on the results of monitoring.

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Section: Neural Network Trainingmentioning

confidence: 99%

Monitoring of solenoid parameters based on neural networks and optical fiber squeezer for solenoid valves diagnosis

Zahidi

Amrane

Azami

et al. 2021

IJECE

View full text Add to dashboard Cite

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“…where e mean represents the mean strain, r 0 represents the initial stress, N is the current number of cycles, j m and m are material constants. Unknown material parameters can be determined by using an optimization algorithm [50] with one set of test data. Once the entropy generated is evaluated, one can easily calculate the corresponding damage, D using Eq.…”

Section: Prediction Of Mean Stress Evolutionmentioning

confidence: 99%

Cyclic Electro-Mechanical Modelling of Uniaxially Stretched PVDF

Lambadi

Jamal

Rao

2021

Trans Indian Inst Met

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Polyvinylidene fluoride (PVDF) is a piezopolymer, and it has numerous applications as sensors and actuators. Uniaxially stretched PVDF subjected to cyclic stresses finds usage in different applications. Characterising the cyclic response of PVDF through appropriate models is important. In this study, we used two approaches to model cyclic stress with the number of cycles. In the first approach, the evolution equations are used and adopted to model the variation of the cyclic mechanical response of PVDF to track the cyclic response at each time step. In the second approach, the effect of cyclic loading on the material, in terms of entropy generation for mechanical and electrical response, is considered for modelling and this approach is adopted to model the PVDF response when subjected to cyclic loading. The mean stress and voltage variation with the number of cycles are predicted.

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“…Levenberg-Marquardt algorithm is an optimization algorithm that combines gradient descent and Gauss-Newton methods [27]. In addition, it is a very efficient technique to find the minima and it performs well on most non-linear functions.…”

Section: Extrinsic Parameters Optimizationmentioning

confidence: 99%

Extrinsic LiDAR/Ground Calibration Method Using 3D Geometrical Plane-Based Estimation

Zaiter

Lherbier

Faour

et al. 2020

Sensors

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This paper details a new extrinsic calibration method for scanning laser rangefinder that is precisely focused on the geometrical ground plane-based estimation. This method is also efficient in the challenging experimental configuration of a high angle of inclination of the LiDAR. In this configuration, the calibration of the LiDAR sensor is a key problem that can be be found in various domains and in particular to guarantee the efficiency of ground surface object detection. The proposed extrinsic calibration method can be summarized by the following procedure steps: fitting ground plane, extrinsic parameters estimation (3D orientation angles and altitude), and extrinsic parameters optimization. Finally, the results are presented in terms of precision and robustness against the variation of LiDAR’s orientation and range accuracy, respectively, showing the stability and the accuracy of the proposed extrinsic calibration method, which was validated through numerical simulation and real data to prove the method performance.

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Parameter extraction and optimization using Levenberg-Marquardt algorithm

Cited by 28 publications

References 5 publications

Monitoring of solenoid parameters based on neural networks and optical fiber squeezer for solenoid valves diagnosis

Monitoring of solenoid parameters based on neural networks and optical fiber squeezer for solenoid valves diagnosis

Cyclic Electro-Mechanical Modelling of Uniaxially Stretched PVDF

Extrinsic LiDAR/Ground Calibration Method Using 3D Geometrical Plane-Based Estimation

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