Inverse sensitivity method for asymmetric multivariable nondefective systems

AIAA Journal ◽  
2001 ◽  
Vol 39 ◽  
pp. 1600-1607
Author(s):  
L. F. Chen ◽  
H. W. Song ◽  
W. L. Wang
Keyword(s):  
Sensitivity Method

3-D optimal design of laminated yoke of billet heater for rolling wire rod using ON/OFF method

2012 ◽  
Vol 61 (1) ◽  
pp. 115-123 ◽  
Author(s):  
Norio Takahashi ◽  
Shunsuke Nakazaki ◽  
Daisuke Miyagi ◽  
Naoki Uchida ◽  
Keiji Kawanaka ◽  
...  
Keyword(s):  
Optimal Design ◽  
Local Heating ◽  
Optimization Method ◽  
Optimal Topology ◽  
Field Problem ◽  
Heating Efficiency ◽  
Magnetic Devices ◽  
Wire Rod ◽  
Magnetic Recording Heads ◽  
Sensitivity Method

3-D optimal design of laminated yoke of billet heater for rolling wire rod using ON/OFF method The optimization method using the ON/OFF sensitivity analysis has an advantage that an epoch-making construction of magnetic circuit may be obtained. Therefore, it is attractive for designers of magnetic devices. We have already developed the ON/OFF method for the optimization of a static magnetic field problem, and the effectiveness is verified by applying it to the optimization of magnetic recording heads. In this paper, the ON/OFF sensitivity method is extended to the optimization of the eddy current problem using the adjoint variable. The newly developed ON/OFF method is applied to the determination of the optimal topology of the yoke of the billet heater for rolling wire rod. As a result, the optimal shape of yoke, which we could not imagine beforehand can be obtained. It is shown that the local heating of the yoke was reduced without decreasing the heating efficiency.

Inverse sensitivity method to self-adjoint systems with repeated eigenvalues

AIAA Journal ◽  
1999 ◽  
Vol 37 ◽  
pp. 933-938
Author(s):  
H. W. Song ◽  
L. F. Chen ◽  
W. L. Wang
Keyword(s):  
Adjoint Systems ◽  
Repeated Eigenvalues ◽  
Sensitivity Method

scholarly journals Application of the topological sensitivity method to the reconstruction of plasma equilibrium domain in a tokamak

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Mohamed Abdelwahed ◽  
Nejmeddine Chorfi ◽  
Maatoug Hassine ◽  
Imen Kallel
Keyword(s):  
Inverse Problem ◽  
Asymptotic Expansion ◽  
Numerical Algorithm ◽  
Shape Function ◽  
Optimization Technique ◽  
Plasma Equilibrium ◽  
Topological Sensitivity ◽  
Sensitivity Method

AbstractThe topological sensitivity method is an optimization technique used in different inverse problem solutions. In this work, we adapt this method to the identification of plasma domain in a Tokamak. An asymptotic expansion of a considered shape function is established and used to solve this inverse problem. Finally, a numerical algorithm is developed and tested in different configurations.

scholarly journals Novel sensitivity method for evaluating the first derivative of the feed-forward neural network outputs

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ravi Kiran ◽  
Dayakar L. Naik
Keyword(s):  
Neural Network ◽  
Feedforward Neural Network ◽  
Analytical Quality ◽  
Feed Forward Neural Network ◽  
First Derivative ◽  
Network Output ◽  
Novel Method ◽  
Classification Tasks ◽  
Trained Neural Network ◽  
Sensitivity Method

AbstractEvaluating the exact first derivative of a feedforward neural network (FFNN) output with respect to the input feature is pivotal for performing the sensitivity analysis of the trained neural network with respect to the inputs. In this paper, a novel method is presented that computes the analytical quality first derivative of a trained feedforward neural network output with respect to the input features without the need for backpropagation. To this end, the complex step derivative approximation is illustrated, and its implementation in the framework of the feedforward neural network is described. Artificial datasets are generated, and the efficacy of the proposed method for both regression and classification tasks is demonstrated. The results obtained for the regression task indicated that the proposed method is capable of obtaining analytical quality derivatives, and in the case of the classification task, the least relevant features could be identified.

scholarly journals An Improved Sensitivity Method for the Simultaneous Identification of Unknown Parameters and External Loads of Nonlinear Structures

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Chongwen Wang ◽  
Chengbin Du
Keyword(s):  
Regularization Parameter ◽  
Parameter Distribution ◽  
Nonlinear Structures ◽  
Noise Levels ◽  
Unknown Parameters ◽  
Key Issues ◽  
The Difference ◽  
Sensitivity Method ◽  
Simultaneous Identification ◽  
External Loads

Because structures may be subject to unknown loads and may simultaneously involve unknown parameters and because simple load identification or parameter identification algorithms cannot be applied under such conditions, it is necessary to seek algorithms that can simultaneously identify unknown parameters and external loads of structures. The sensitivity method is one of them, and this paper extends this method to nonlinear structures. In addition, the key issues associated with the sensitivity method are systematically studied, and suggestions for improvement are put forward, including the use of the difference method instead of the derivative method to calculate the sensitivity, the use of a fixed regularization parameter instead of the traditional regularization parameter calculation methods, and measures for guarantee of iterative convergence. The improved sensitivity method is applied to two types of nonlinear structures, and the effects of the regularization parameter, distribution of measured points, response types, noise levels, and the magnitude of the perturbation on the identified results are discussed.

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