Solution of inverse heat conduction problems using Kalman filter-enhanced Bayesian back propagation neural network data fusion

2007 ◽  
Vol 50 (11-12) ◽  
pp. 2089-2100 ◽  
Author(s):  
S. Deng ◽  
Y. Hwang
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Heat Conduction ◽  
Data Fusion ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Data ◽  
Inverse Heat Conduction ◽  
Inverse Heat Conduction Problems

Combined Multi-Scale Sensing and Neural Network Data Fusion for Damage Identification

Aerospace ◽  
2003 ◽  
Author(s):  
Michel Studer ◽  
Kara Peters
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Measurement Errors ◽  
Damage Identification ◽  
Back Propagation ◽  
Position Angle ◽  
Back Propagation Neural Network ◽  
Network Data ◽  
Multi Scale ◽  
The Neural Network

Multi-scale measurements, i.e. measurements of strain, strain gradient and integrated strain data, throughout a structural volume have demonstrated a great potential for improved damage identification. However, the large number of data and their different forms make fusion of the data difficult. To overcome this problem, a neural network data fusion approach is proposed. A simulation of damage identification in an isotropic cracked plate is presented. The crack position, angle and crack length are used as test parameters to be determined. A back-propagation neural network is trained to reproduce the crack angle and length as a function of all sensor responses. The improvement gained by using both multi-scale sensing and neural network data fusion for this specific case is significant. Testing of the sensitivity of the method to measurement errors or missing data demonstrated the robustness of the neural network to errors.

Applying Neural Networks to the Solution of the Inverse Heat Conduction Problem in a Gun Barrel

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Y. Hwang ◽  
S. Deng
Keyword(s):  
Neural Network ◽  
Heat Flux ◽  
Heat Conduction ◽  
Heat Conduction Problem ◽  
Nonlinear Function ◽  
Inverse Heat Conduction ◽  
Unknown Parameters ◽  
Acceptable Error ◽  
Gun Barrel ◽  
Inverse Heat Conduction Problems

The primary cause of gun barrel erosion is the heat generated by the shell as its travels along the barrel. Therefore, calculating the heat flux input to the gun bore is very important when investigating wear problems in the gun barrel and examining its thermomechanical properties. This paper employs the continuous-time analog Hopfield neural network (CHNN) to compute the temperature distribution in various forward heat conduction problems. An efficient technique is then proposed for the solution of inverse heat conduction problems using a three-layered backpropagation neural network (BPN). The weak generalization capacity of BPN networks when applied to the solution of nonlinear function approximations is improved by employing the Bayesian regularization algorithm. The CHNN scheme is used to calculate the temperature in a 155mm gun barrel and the trained BPN is then used to estimate the heat flux of the inner surface of the barrel. The results show that the proposed neural network analysis method successfully solves forward heat conduction problems and is capable of predicting the unknown parameters in inverse problems with an acceptable error.

Inversion Algorithm Based on the Unscented Kalman Filter for Inverse Heat Conduction Problems

2013 ◽  
Vol 705 ◽  
pp. 474-482
Author(s):  
Pan Chu
Keyword(s):  
Kalman Filter ◽  
Heat Conduction ◽  
Unscented Kalman Filter ◽  
Initial Conditions ◽  
Measurement Data ◽  
Measurement Information ◽  
Inverse Heat Conduction ◽  
Space Problem ◽  
Inversion Model ◽  
Inverse Heat Conduction Problems

The inverse heat conduction problems (IHCP) analysis method provides a promising approach for acquiring the thermal physical properties of materials, the boundary conditions and the initial conditions from the known temperature measurement data, where the efficiency of the inversion algorithms plays a crucial role in real applications. In this paper, an inversion model that simultaneously utilizes the process evolution information of the objects to be estimated and the measurement information is proposed. The original IHCP is formulated into a state-space problem, and the unscented Kalman filter (UKF) method is developed for solving the proposed inversion model. The implementation of the proposed method does not require the gradient vector, the Jacobian matrix or the Hessian matrix, and thus the computational complexity is decreased. Numerical simulations are implemented to evaluate the feasibility of the proposed algorithm. For the cases simulated in this paper, satisfactory results are obtained, which indicates that the proposed algorithm is successful in solving the IHCP.

Combined estimation of the state of charge of a lithium battery based on a back-propagation– adaptive Kalman filter algorithm

2017 ◽  
Vol 232 (3) ◽  
pp. 357-366 ◽  
Author(s):  
Wanzhong Zhao ◽  
Xiangchuang Kong ◽  
Chunyan Wang
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
The State ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Adaptive Kalman Filter ◽  
Precise Estimation ◽  
Kalman Filter Algorithm

The precise estimation of the battery’s state of charge is one of the most significant and difficult techniques for battery management systems. In order to improve the accuracy of estimation of the state of charge, the forgetting-factor recursive least-squares method is used to achieve online identification of the model parameters based on the first-order RC battery model, and a back-propagation neural-network-assisted adaptive Kalman filter algorithm is proposed. A back-propagation neural network is established by using the MATLAB neural network toolbox and is trained offline on the basis of the battery test data; then the trained back-propagation neural network is used to realize the online optimized results of an adaptive Kalman filter algorithm for estimation of the state of charge. The proposed methodology for estimation of the state of charge is demonstrated using experimental lithium-ion battery module data in dynamic stress tests. The results indicate that, in comparison with the common adaptive Kalman filter algorithm, the back-propagation–adaptive Kalman filter algorithm significantly improved precise estimation of the state of charge.

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