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Badong Chen - One of the best experts on this subject based on the ideXlab platform.

  • bias compensated normalized maximum correntropy criterion algorithm for system identification with noisy input
    Signal Processing, 2018
    Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Badong Chen
    Abstract:

    Abstract This paper proposes a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy Based Cost Function, which is rather robust with respect to impulsive noises. To deal with the noisy input, we introduce a bias-compensated vector to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the bias-compensated vector. Taking advantage of the bias-compensated vector, the bias caused by the input noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive output noise environment.

  • bias compensated normalized maximum correntropy criterion algorithm for system identification with noisy input
    arXiv: Machine Learning, 2017
    Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Badong Chen
    Abstract:

    This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy Based Cost Function, which is rather robust with respect to impulsive noises. To deal with the noisy input, we introduce a bias-compensated vector (BCV) to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the BCV. Taking advantage of the BCV, the bias caused by the input noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive output noise environment.

Dongqiao Zheng - One of the best experts on this subject based on the ideXlab platform.

  • bias compensated normalized maximum correntropy criterion algorithm for system identification with noisy input
    Signal Processing, 2018
    Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Badong Chen
    Abstract:

    Abstract This paper proposes a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy Based Cost Function, which is rather robust with respect to impulsive noises. To deal with the noisy input, we introduce a bias-compensated vector to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the bias-compensated vector. Taking advantage of the bias-compensated vector, the bias caused by the input noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive output noise environment.

  • bias compensated normalized maximum correntropy criterion algorithm for system identification with noisy input
    arXiv: Machine Learning, 2017
    Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Badong Chen
    Abstract:

    This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy Based Cost Function, which is rather robust with respect to impulsive noises. To deal with the noisy input, we introduce a bias-compensated vector (BCV) to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the BCV. Taking advantage of the BCV, the bias caused by the input noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive output noise environment.

Zhiyu Zhang - One of the best experts on this subject based on the ideXlab platform.

  • bias compensated normalized maximum correntropy criterion algorithm for system identification with noisy input
    Signal Processing, 2018
    Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Badong Chen
    Abstract:

    Abstract This paper proposes a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy Based Cost Function, which is rather robust with respect to impulsive noises. To deal with the noisy input, we introduce a bias-compensated vector to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the bias-compensated vector. Taking advantage of the bias-compensated vector, the bias caused by the input noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive output noise environment.

  • bias compensated normalized maximum correntropy criterion algorithm for system identification with noisy input
    arXiv: Machine Learning, 2017
    Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Badong Chen
    Abstract:

    This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy Based Cost Function, which is rather robust with respect to impulsive noises. To deal with the noisy input, we introduce a bias-compensated vector (BCV) to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the BCV. Taking advantage of the BCV, the bias caused by the input noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive output noise environment.

Saeid Nahavandi - One of the best experts on this subject based on the ideXlab platform.

  • a new fuzzy Based combined prediction interval for wind power forecasting
    IEEE Transactions on Power Systems, 2016
    Co-Authors: Abdollah Kavousifard, Abbas Khosravi, Saeid Nahavandi
    Abstract:

    This paper makes use of the idea of prediction intervals (PIs) to capture the uncertainty associated with wind power generation in power systems. Since the forecasting errors cannot be appropriately modeled using distribution probability Functions, here we employ a powerful nonparametric approach called lower upper bound estimation (LUBE) method to construct the PIs. The proposed LUBE method uses a new framework Based on a combination of PIs to overcome the performance instability of neural networks (NNs) used in the LUBE method. Also, a new fuzzy-Based Cost Function is proposed with the purpose of having more freedom and flexibility in adjusting NN parameters used for construction of PIs. In comparison with the other Cost Functions in the literature, this new formulation allows the decision-makers to apply their preferences for satisfying the PI coverage probability and PI normalized average width individually. As the optimization tool, bat algorithm with a new modification is introduced to solve the problem. The feasibility and satisfying performance of the proposed method are examined using datasets taken from different wind farms in Australia.

  • prediction interval Based neural network modelling of polystyrene polymerization reactor a new perspective of data Based modelling
    World Congress on Engineering, 2014
    Co-Authors: Mohammad Anwar Hosen, Abbas Khosravi, Saeid Nahavandi, Douglas Creighton
    Abstract:

    Abstract In this paper, prediction interval (PI)-Based modelling techniques are introduced and applied to capture the nonlinear dynamics of a polystyrene batch reactor system. Traditional NN models are developed using experimental datasets with and without disturbances. Simulation results indicate that traditional NNs cannot properly handle disturbances in reactor data and demonstrate a poor forecasting performance, with an average MAPE of 22% in the presence of disturbances. The lower upper bound estimation (LUBE) method is applied for the construction of PIs to quantify uncertainties associated with forecasts. The simulated annealing optimization technique is employed to adjust NN parameters for minimization of an innovative PI-Based Cost Function. The simulation results reveal that the LUBE method generates quality PIs without requiring prohibitive computations. As both calibration and sharpness of PIs are practically and theoretically satisfactory, the constructed PIs can be used as part of the decision-making and control process of polymerization reactors.

  • a genetic algorithm Based method for improving quality of travel time prediction intervals
    Transportation Research Part C-emerging Technologies, 2011
    Co-Authors: Abbas Khosravi, Saeid Nahavandi, Ehsan Mazloumi, Doug Creighton, J W C Van Lint
    Abstract:

    Abstract The transportation literature is rich in the application of neural networks for travel time prediction. The uncertainty prevailing in operation of transportation systems, however, highly degrades prediction performance of neural networks. Prediction intervals for neural network outcomes can properly represent the uncertainty associated with the predictions. This paper studies an application of the delta technique for the construction of prediction intervals for bus and freeway travel times. The quality of these intervals strongly depends on the neural network structure and a training hyperparameter. A genetic algorithm–Based method is developed that automates the neural network model selection and adjustment of the hyperparameter. Model selection and parameter adjustment is carried out through minimization of a prediction interval-Based Cost Function, which depends on the width and coverage probability of constructed prediction intervals. Experiments conducted using the bus and freeway travel time datasets demonstrate the suitability of the proposed method for improving the quality of constructed prediction intervals in terms of their length and coverage probability.

  • prediction interval construction and optimization for adaptive neurofuzzy inference systems
    IEEE Transactions on Fuzzy Systems, 2011
    Co-Authors: Abbas Khosravi, Saeid Nahavandi, Douglas Creighton
    Abstract:

    The performance of an adaptive neurofuzzy inference system (ANFIS) significantly drops when uncertainty exists in the data or system operation. Prediction intervals (PIs) can quantify the uncertainty associated with ANFIS point predictions. This paper first presents a methodology to adapt the delta technique for the construction of PIs for outcomes of the ANFIS models. As the ANFIS models are linear in their consequent part, the ANFIS-Based PIs are computationally less expensive than neural network (NN)-Based PIs. Second, this paper proposes a method to optimize ANFIS-Based PIs. A new PI-Based Cost Function is developed for the training of the ANFIS models. A simulated annealing-Based algorithm is applied to minimize the new nonlinear Cost Function and adjust the premise and consequent parameters of the ANFIS model. Using three real-world case studies, it is shown that ANFIS-Based PIs are computationally less expensive than NN-Based PIs. The application of the proposed optimization algorithm leads to better quality PIs than optimized NN-Based PIs.

  • comprehensive review of neural network Based prediction intervals and new advances
    IEEE Transactions on Neural Networks, 2011
    Co-Authors: Abbas Khosravi, Saeid Nahavandi, Douglas Creighton, Amir F Atiya
    Abstract:

    This paper evaluates the four leading techniques proposed in the literature for construction of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian, bootstrap, and mean-variance estimation (MVE) methods are reviewed and their performance for generating high-quality PIs is compared. PI-Based measures are proposed and applied for the objective and quantitative assessment of each method's performance. A selection of 12 synthetic and real-world case studies is used to examine each method's performance for PI construction. The comparison is performed on the basis of the quality of generated PIs, the repeatability of the results, the computational requirements and the PIs variability with regard to the data uncertainty. The obtained results in this paper indicate that: 1) the delta and Bayesian methods are the best in terms of quality and repeatability, and 2) the MVE and bootstrap methods are the best in terms of low computational load and the width variability of PIs. This paper also introduces the concept of combinations of PIs, and proposes a new method for generating combined PIs using the traditional PIs. Genetic algorithm is applied for adjusting the combiner parameters through minimization of a PI-Based Cost Function subject to two sets of restrictions. It is shown that the quality of PIs produced by the combiners is dramatically better than the quality of PIs obtained from each individual method.

Jeffrey A Fessler - One of the best experts on this subject based on the ideXlab platform.

  • pwls ultra an efficient clustering and learning Based approach for low dose 3d ct image reconstruction
    IEEE Transactions on Medical Imaging, 2018
    Co-Authors: Xuehang Zheng, Saiprasad Ravishankar, Yong Long, Jeffrey A Fessler
    Abstract:

    The development of computed tomography (CT) image reconstruction methods that significantly reduce patient radiation exposure, while maintaining high image quality is an important area of research in low-dose CT imaging. We propose a new penalized weighted least squares (PWLS) reconstruction method that exploits regularization Based on an efficient Union of Learned TRAnsforms (PWLS-ULTRA). The union of square transforms is pre-learned from numerous image patches extracted from a dataset of CT images or volumes. The proposed PWLS-Based Cost Function is optimized by alternating between a CT image reconstruction step, and a sparse coding and clustering step. The CT image reconstruction step is accelerated by a relaxed linearized augmented Lagrangian method with ordered-subsets that reduces the number of forward and back projections. Simulations with 2-D and 3-D axial CT scans of the extended cardiac-torso phantom and 3-D helical chest and abdomen scans show that for both normal-dose and low-dose levels, the proposed method significantly improves the quality of reconstructed images compared to PWLS reconstruction with a nonadaptive edge-preserving regularizer. PWLS with regularization Based on a union of learned transforms leads to better image reconstructions than using a single learned square transform. We also incorporate patch-Based weights in PWLS-ULTRA that enhance image quality and help improve image resolution uniformity. The proposed approach achieves comparable or better image quality compared to learned overcomplete synthesis dictionaries, but importantly, is much faster (computationally more efficient).

  • pwls ultra an efficient clustering and learning Based approach for low dose 3d ct image reconstruction
    arXiv: Machine Learning, 2017
    Co-Authors: Xuehang Zheng, Saiprasad Ravishankar, Yong Long, Jeffrey A Fessler
    Abstract:

    The development of computed tomography (CT) image reconstruction methods that significantly reduce patient radiation exposure while maintaining high image quality is an important area of research in low-dose CT (LDCT) imaging. We propose a new penalized weighted least squares (PWLS) reconstruction method that exploits regularization Based on an efficient Union of Learned TRAnsforms (PWLS-ULTRA). The union of square transforms is pre-learned from numerous image patches extracted from a dataset of CT images or volumes. The proposed PWLS-Based Cost Function is optimized by alternating between a CT image reconstruction step, and a sparse coding and clustering step. The CT image reconstruction step is accelerated by a relaxed linearized augmented Lagrangian method with ordered-subsets that reduces the number of forward and back projections. Simulations with 2-D and 3-D axial CT scans of the extended cardiac-torso phantom and 3D helical chest and abdomen scans show that for both normal-dose and low-dose levels, the proposed method significantly improves the quality of reconstructed images compared to PWLS reconstruction with a nonadaptive edge-preserving regularizer (PWLS-EP). PWLS with regularization Based on a union of learned transforms leads to better image reconstructions than using a single learned square transform. We also incorporate patch-Based weights in PWLS-ULTRA that enhance image quality and help improve image resolution uniformity. The proposed approach achieves comparable or better image quality compared to learned overcomplete synthesis dictionaries, but importantly, is much faster (computationally more efficient).