To identify a system with non-uniformly sampled data, a recursive bayesian algorithm combined dynamic filter with covariance resetting is proposed. First, the input-output data is filtered by the estimated noise trans...
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To identify a system with non-uniformly sampled data, a recursive bayesian algorithm combined dynamic filter with covariance resetting is proposed. First, the input-output data is filtered by the estimated noise transfer function, and the system is decomposed into two fictitious sub-systems with a low dimension. Second, the estimated variance of the noise is employed in the proposed algorithm to improve the estimates. Furthermore, an efficient covariance resetting strategy is integrated into the algorithm to increase the convergence rate. Finally, the proposed algorithm is validated by a numeric example.
To identify the Box-Jenkins systems with non-uniformly sampled input data, a recursive bayesian algorithm with covariance resetting was proposed in this paper. Considering the prior probability density functions of pa...
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To identify the Box-Jenkins systems with non-uniformly sampled input data, a recursive bayesian algorithm with covariance resetting was proposed in this paper. Considering the prior probability density functions of parameters and the observed input-output data, the parameters were estimated by maximizing the posterior probability distribution function. During the estimation, the variance of the noise was taken as a weighting factor, and the proposed algorithm was formulated as a weighted least squares. As a result, the accuracy of the estimates increased. Meanwhile, a modified covariance resetting strategy was integrated into the algorithm to improve the convergence rate, and the convergence of the algorithm was also analyzed. A simulation example was applied to validate the proposed algorithm.
A novel recursive bayesian algorithm based on a variable-knot spline approximation is proposed to identify Wiener systems with process *** this algorithm,a spline function is used to approximate the inverse function o...
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A novel recursive bayesian algorithm based on a variable-knot spline approximation is proposed to identify Wiener systems with process *** this algorithm,a spline function is used to approximate the inverse function of the nonlinear part,which overcomes the drawbacks of a traditional polynomial function,such as poor extrapolation and potential oscillatory behaviour.A mean-value-based knot-selection method is used to achieve estimates with high *** order to improve the accuracy of the spline approximation,a new knot-variation strategy is also employed in the *** proposed algorithm is validated by a numerical simulation.
To identify the OEAR model with non-uniformly sampled input data, a recursivebayesian identification algorithm with covariance rescuing is proposed in this paper. Comparing with the conventional recursive least squar...
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ISBN:
(纸本)9781479970162
To identify the OEAR model with non-uniformly sampled input data, a recursivebayesian identification algorithm with covariance rescuing is proposed in this paper. Comparing with the conventional recursive least squares algorithm based on auxiliary model, the presented algorithm considers the variance of the colored noise and can estimate the parameter with high accuracy. Furthermore, the algorithm integrates the prior probability density function of the parameters and the prior probability density function of the process data together, and achieves better performance than the maximum likelihood algorithm. To improve the convergence rate, a new covariance resetting method is also integrated in the algorithm. A simulation example demonstrates the performance of the proposed algorithm.
To identify the OEAR model with non-uniformly sampled input data,a recursivebayesian identification algorithm with covariance resetting is proposed in this *** with the conventional recursive least squares algorithm ...
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ISBN:
(纸本)9781479970186
To identify the OEAR model with non-uniformly sampled input data,a recursivebayesian identification algorithm with covariance resetting is proposed in this *** with the conventional recursive least squares algorithm based on auxiliary model,the presented algorithm considers the variance of the colored noise and can estimate the parameter with high ***,the algorithm integrates the prior probability density function of the parameters and the prior probability density function of the process data together,and achieves better performance than the maximum likelihood *** improve the convergence rate,a new covariance resetting method is also integrated in the algorithm.A simulation example demonstrates the performance of the proposed algorithm.
Multimedia Super-Resolution (SR) reconstruction is an essential and mandatory process for different visualization functions. Recently, several schemes have been suggested for single- and multi-image SR reconstruction....
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Multimedia Super-Resolution (SR) reconstruction is an essential and mandatory process for different visualization functions. Recently, several schemes have been suggested for single- and multi-image SR reconstruction. This work presents an effective SR reconstruction scheme for visual quality and resolution enhancement of 3D Video (3DV) sequences. The idea behind the proposed 3DV SR reconstruction scheme is the utilization of a recursive bayesian algorithm for improving the resolution of the degraded 3DV sequences with down-sampling, blurring, and noise effects. In addition, a significant stage of histogram matching based on a visual image with a better-distributed histogram is employed. The main aim of employing the histogram matching stage for enhancing the 3DV sequence is to introduce a dynamic range modification of each 3DV frame. Hence, it presents a 3DV sequence with an enhanced distribution of intensities. This improves the whole performance efficiency of the suggested scheme. The performance of the proposed SR reconstruction scheme is compared with that of the conventional bicubic interpolation scheme. Comparisons with recent and related SR reconstruction schemes are also introduced. Simulation results reveal that the proposed scheme achieves superior outcomes in terms of Structural Similarity (SSIM) index, local contrast, average gradient, Mean Square Error (MSE), edge intensity, entropy, and Peak Signal-to-Noise Ratio (PSNR) of the resulting 3DV frames.
To identify systems with Non-uniformly sampled input data, a filter based recursive identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the algo...
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To identify systems with Non-uniformly sampled input data, a filter based recursive identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the algorithm transforms the systems with colored noise into systems with white noise. To obtain improved estimates, the estimated noise variance is employed in the algorithm. Meanwhile a novel covariance resetting method is also integrated in the algorithm to improve the convergence rate. A simulation example validates the proposed algorithm.
Accurate and reliable estimation of battery SOC is critical to enhance its service life and safety, and the accuracy of model parameter identification also directly affects the result of SOC estimation. This paper foc...
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Accurate and reliable estimation of battery SOC is critical to enhance its service life and safety, and the accuracy of model parameter identification also directly affects the result of SOC estimation. This paper focuses on the parameter identification and SOC estimation of the dual-polarization. A co-estimation of recursive bayesian algorithm and adaptive extended Kalman filter algorithm (RB-AEKF) is utilized to dynamically identify the model parameters and estimate the battery SOC. Firstly, the RB algorithm is used to identify model parameters through real-time current and voltage measurement data. The predicted voltage of the dual-polarization model is basically the same as the actual voltage, which better reflects the dynamic characteristics of the battery and the accuracy of the identification algorithm. In addition, adaptive noise variance updating algorithm is added to the extended Kalman filter to improve the accuracy of SOC estimation. Furthermore, a dataset consisting of data from a dynamic stress test (DST) and a federal urban driving schedule (FUDS) is used to verify the proposed method. The SOC estimafte error based on RB-AEKF stays within 2% and root mean square error is 0.01085 under FUDS test. Finally, we conduct the robustness analysis, and the results show that the algorithm has satisfactory robustness against inaccurate initial SOC and different measurement noise covariance.
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