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Feng Ding - One of the best experts on this subject based on the ideXlab platform.
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Data Filtering based maximum likelihood gradient estimation algorithms for a multivariate equation error system with arma noise
Journal of The Franklin Institute-engineering and Applied Mathematics, 2020Co-Authors: Lijuan Liu, Ahmed Alsaedi, Feng Ding, Haibo Liu, Tasawar HayatAbstract:Abstract In this paper, we use the maximum likelihood principle and the Data Filtering technique to study the identification issue of the multivariate equation-error system whose outputs are contaminated by an ARMA noise process. The key is to break the system into several regressive identification subsystems based on the number of the outputs. Then a multivariate equation-error subsystem is transformed into a filtered model and a filtered noise model, and a Filtering based maximum likelihood extended stochastic gradient algorithm is derived to estimate the parameters of these two models. The Filtering based maximum likelihood extended stochastic gradient algorithm has higher parameter estimation accuracy than the maximum likelihood generalized extended stochastic gradient algorithm and the maximum likelihood recursive generalized extended least squares algorithm. The simulation examples indicate that the proposed methods work well.
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maximum likelihood recursive identification for the multivariate equation error autoregressive moving average systems using the Data Filtering
IEEE Access, 2019Co-Authors: Lijuan Liu, Ahmed Alsaedi, Feng Ding, Jian Pan, Tasawar HayatAbstract:The maximum likelihood principle has wide applications in system identification. This paper studies the maximum likelihood identification problems of the multivariate equation-error systems with colored noise. The system is broken down into several subsystems based on the number of the outputs. The key is to transform the subsystem into a controlled autoregressive moving average model and a noise model. Based on the maximum likelihood principle and the Data Filtering technique, a Filtering-based maximum likelihood recursive generalized extended least squares algorithm is presented for estimating the parameters of these two models. For comparison, a maximum likelihood recursive generalized extended least squares algorithm is presented. Finally, the simulation example results confirm the effectiveness of the two algorithms.
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auxiliary model based recursive generalized least squares algorithm for multivariate output error autoregressive systems using the Data Filtering
Circuits Systems and Signal Processing, 2019Co-Authors: Feng DingAbstract:This paper focuses on the parameter estimation problem of multivariate output-error autoregressive systems. Based on the Data Filtering technique and the auxiliary model identification idea, we derive a Filtering-based auxiliary model recursive generalized least squares algorithm. The key is to filter the input–output Data and to derive two identification models, one of which includes the system parameters and the other contains the noise parameters. Compared with the auxiliary model-based recursive generalized least squares algorithm, the proposed algorithm requires less computational burden and can generate more accurate parameter estimates. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm.
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adaptive gradient based iterative algorithm for multivariable controlled autoregressive moving average systems using the Data Filtering technique
Complexity, 2018Co-Authors: Xiao Jiang, Feng Ding, Wenfang DingAbstract:The identification problem of multivariable controlled autoregressive systems with measurement noise in the form of the moving average process is considered in this paper. The key is to filter the input–output Data using the Data Filtering technique and to decompose the identification model into two subidentification models. By using the negative gradient search, an adaptive Data Filtering-based gradient iterative (F-GI) algorithm and an F-GI with finite measurement Data are proposed for identifying the parameters of multivariable controlled autoregressive moving average systems. In the numerical example, we illustrate the effectiveness of the proposed identification methods.
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Recursive least squares identification methods for multivariate pseudo-linear systems using the Data Filtering
Multidimensional Systems and Signal Processing, 2018Co-Authors: Ping Ma, Ahmed Alsaedi, Feng Ding, Tasawar HayatAbstract:This paper concerns the parameter identification methods of multivariate pseudo-linear autoregressive systems. A multivariate recursive generalized least squares algorithm is presented as a comparison. By using the Data Filtering technique, a multivariate pseudo-linear autoregressive system is transformed into a filtered system model and a filtered noise model, and a Filtering based multivariate recursive generalized least squares algorithm is developed for estimating the parameters of these two models. The proposed algorithm achieves a higher computational efficiency than the multivariate recursive generalized least squares algorithm, and the simulation results prove that the proposed method is effective.
Tasawar Hayat - One of the best experts on this subject based on the ideXlab platform.
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Data Filtering based maximum likelihood gradient estimation algorithms for a multivariate equation error system with arma noise
Journal of The Franklin Institute-engineering and Applied Mathematics, 2020Co-Authors: Lijuan Liu, Ahmed Alsaedi, Feng Ding, Haibo Liu, Tasawar HayatAbstract:Abstract In this paper, we use the maximum likelihood principle and the Data Filtering technique to study the identification issue of the multivariate equation-error system whose outputs are contaminated by an ARMA noise process. The key is to break the system into several regressive identification subsystems based on the number of the outputs. Then a multivariate equation-error subsystem is transformed into a filtered model and a filtered noise model, and a Filtering based maximum likelihood extended stochastic gradient algorithm is derived to estimate the parameters of these two models. The Filtering based maximum likelihood extended stochastic gradient algorithm has higher parameter estimation accuracy than the maximum likelihood generalized extended stochastic gradient algorithm and the maximum likelihood recursive generalized extended least squares algorithm. The simulation examples indicate that the proposed methods work well.
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maximum likelihood recursive identification for the multivariate equation error autoregressive moving average systems using the Data Filtering
IEEE Access, 2019Co-Authors: Lijuan Liu, Ahmed Alsaedi, Feng Ding, Jian Pan, Tasawar HayatAbstract:The maximum likelihood principle has wide applications in system identification. This paper studies the maximum likelihood identification problems of the multivariate equation-error systems with colored noise. The system is broken down into several subsystems based on the number of the outputs. The key is to transform the subsystem into a controlled autoregressive moving average model and a noise model. Based on the maximum likelihood principle and the Data Filtering technique, a Filtering-based maximum likelihood recursive generalized extended least squares algorithm is presented for estimating the parameters of these two models. For comparison, a maximum likelihood recursive generalized extended least squares algorithm is presented. Finally, the simulation example results confirm the effectiveness of the two algorithms.
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Recursive least squares identification methods for multivariate pseudo-linear systems using the Data Filtering
Multidimensional Systems and Signal Processing, 2018Co-Authors: Ping Ma, Ahmed Alsaedi, Feng Ding, Tasawar HayatAbstract:This paper concerns the parameter identification methods of multivariate pseudo-linear autoregressive systems. A multivariate recursive generalized least squares algorithm is presented as a comparison. By using the Data Filtering technique, a multivariate pseudo-linear autoregressive system is transformed into a filtered system model and a filtered noise model, and a Filtering based multivariate recursive generalized least squares algorithm is developed for estimating the parameters of these two models. The proposed algorithm achieves a higher computational efficiency than the multivariate recursive generalized least squares algorithm, and the simulation results prove that the proposed method is effective.
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Data Filtering based maximum likelihood extended gradient method for multivariable systems with autoregressive moving average noise
Journal of The Franklin Institute-engineering and Applied Mathematics, 2018Co-Authors: Tasawar Hayat, Feng Ding, Feiyan Chen, Ling XuAbstract:Abstract For multivariable systems with autoregressive moving average noises, we decompose the multivariable system into m subsystems (m denotes the number of outputs) and present a maximum likelihood generalized extended gradient algorithm and a Data Filtering based maximum likelihood extended gradient algorithm to estimate the parameter vectors of these subsystems. By combining the maximum likelihood principle and the Data Filtering technique, the proposed algorithms are effective and have computational advantages over existing estimation algorithms. Finally, a numerical simulation example is given to support the developed methods and to show their effectiveness.
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Data Filtering based multi innovation extended gradient method for controlled autoregressive autoregressive moving average systems using the maximum likelihood principle
Mathematics and Computers in Simulation, 2017Co-Authors: Feiyan Chen, Ahmed Alsaedi, Tasawar Hayat, Feng DingAbstract:This paper combines the Data Filtering technique with the maximum likelihood principle for parameter estimation of controlled autoregressive ARMA (autoregressive moving average) systems. We use an estimated noise transfer function to filter the input–output Data and derive a Filtering based maximum likelihood multi-innovation extended gradient algorithm to estimate the parameters of the systems by replacing the unmeasurable variables in the information vectors with their estimates. A maximum likelihood generalized extended gradient algorithm is given for comparison. A numerical simulation is given to support the developed methods.
Yanjiao Wang - One of the best experts on this subject based on the ideXlab platform.
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a recursive least squares parameter estimation algorithm for output nonlinear autoregressive systems using the input output Data Filtering
Journal of The Franklin Institute-engineering and Applied Mathematics, 2017Co-Authors: Feng Ding, Yanjiao Wang, Jiyang Dai, Qijia ChenAbstract:Abstract Nonlinear systems exist widely in industrial processes. This paper studies the parameter estimation methods of establishing the mathematical models for a class of output nonlinear systems, whose output is nonlinear about the past outputs and linear about the inputs. We use an estimated noise transfer function to filter the input–output Data and obtain two identification models, one containing the parameters of the system model, and the other containing the parameters of the noise model. Based on the Data Filtering technique, a Data Filtering based recursive least squares algorithm is proposed. The simulation results show that the proposed algorithm can generate more accurate parameter estimates than the recursive generalized least squares algorithm.
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recursive least squares algorithm and gradient algorithm for hammerstein wiener systems using the Data Filtering
Nonlinear Dynamics, 2016Co-Authors: Yanjiao Wang, Feng DingAbstract:This paper considers the parameter estimation problems of Hammerstein–Wiener systems by using the Data Filtering technique. In order to improve the estimation accuracy, the Data Filtering-based recursive generalized extended least squares algorithm is derived. In order to improve the computational efficiency, the Data Filtering-based generalized extended stochastic gradient algorithm is derived for estimating the system parameters. Finally, the computational efficiency of the proposed algorithms is analyzed and compared. The simulation results indicate that the proposed algorithms can effectively estimate the parameters of Hammerstein–Wiener systems.
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Recursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the Data Filtering
Nonlinear Dynamics, 2015Co-Authors: Yanjiao Wang, Feng DingAbstract:This paper considers the parameter estimation problems of Hammerstein–Wiener systems by using the Data Filtering technique. In order to improve the estimation accuracy, the Data Filtering-based recursive generalized extended least squares algorithm is derived. In order to improve the computational efficiency, the Data Filtering-based generalized extended stochastic gradient algorithm is derived for estimating the system parameters. Finally, the computational efficiency of the proposed algorithms is analyzed and compared. The simulation results indicate that the proposed algorithms can effectively estimate the parameters of Hammerstein–Wiener systems.
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Data Filtering based parameter estimation algorithms for multivariable Box-Jenkins-like systems
IFAC-PapersOnLine, 2015Co-Authors: Yanjiao Wang, Feng DingAbstract:Abstract This paper proposes an auxiliary model based hierarchical least squares algorithm for multivariable Box-Jenkins-like systems using the hierarchical identification principle. To improve the computational efficiency, a multivariable system is decomposed into two subsystems by using the Data Filtering technique. Furthermore, this paper presents a Data Filtering based auxiliary model hierarchical least squares algorithm for multivariable Box-Jenkins-like systems. The simulation example shows that the proposed identification algorithms are effective.
Jae-woo Chang - One of the best experts on this subject based on the ideXlab platform.
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APSCC - A Sampling-Based Data Filtering Scheme for Reducing Energy Consumption in Wireless Sensor Networks
2011 IEEE Asia-Pacific Services Computing Conference, 2011Co-Authors: Seung-tae Hong, Jae-woo ChangAbstract:Wireless sensor networks (WSN) are widely used in the various monitoring systems. When implementing WSN-based monitoring systems, there are three important issues to be considered. At first, we should consider a node failure detection method to provide continuous monitoring. Secondly, because sensor nodes use limited battery power, we need an efficient Data Filtering method to reduce energy consumption. At last, we should consider a Data Filtering method for reducing processing overhead. The existing Kalman Filtering scheme has good performance on Data Filtering, but it causes too much processing overhead for estimating sensed Data. To solve this problem, we, in this paper, propose a sampling-based Data Filtering scheme based on statistical Data analysis. First, our scheme periodically aggregates nodes' survival massages to support node failure detection. Secondly, to reduce energy consumption, our scheme sends the sampled Data including node survival massage and perform Data Filtering based on the messages. Finally, it analyzes the sampled Data to estimate Filtering range at a server. As a result, each sensor node can use only a simple compare operation for Filtering Data. Through performance analysis, we show that our scheme outperforms the Kalman Filtering scheme in terms of the number of Data transmissions.
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HPCC - A New Data Filtering Scheme Based on Statistical Data Analysis for Monitoring Systems in Wireless Sensor Networks
2011 IEEE International Conference on High Performance Computing and Communications, 2011Co-Authors: Seung-tae Hong, Jae-woo ChangAbstract:Recently, wireless sensor networks (WSN) are actively used for various monitoring systems. While implementing WSN-based monitoring systems, there are three important issues to be considered. At first, we should consider a node failure detection method to provide continuous monitoring. Secondly, because sensor nodes use limited battery power, we need an efficient Data Filtering method to reduce energy consumption. At last, we should consider a Data Filtering method for reducing processing overhead. The existing Kalman Filtering scheme has good performance on Data Filtering, but it causes too much processing overhead for estimating sensed Data. To solve this problem, we, in this paper, propose a new Data Filtering scheme based on statistical Data analysis. First, the proposed scheme periodically aggregates nodes' survival massages to support node failure detection. Secondly, to reduce energy consumption, the proposed scheme sends the sample Data including node survival massage and perform Data Filtering based on the messages. Finally, it analyzes the sample Data to estimate Filtering range at a server. As a result, each sensor node can use only a simple compare operation for Filtering Data. Through performance analysis, we show that the proposed scheme outperforms the Kalman Filtering scheme in terms of the number of messages transmission.
Erfu Yang - One of the best experts on this subject based on the ideXlab platform.
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Data Filtering-based least squares iterative algorithm for Hammerstein nonlinear systems by using the model decomposition
Nonlinear Dynamics, 2015Co-Authors: Feng Ding, Erfu YangAbstract:This paper focuses on the iterative identification problems for a class of Hammerstein nonlinear systems. By decomposing the system into two fictitious subsystems, a decomposition-based least squares iterative algorithm is presented for estimating the parameter vector in each subsystem. Moreover, a Data Filtering-based decomposition least squares iterative algorithm is proposed. The simulation results indicate that the Data Filtering-based least squares iterative algorithm can generate more accurate parameter estimates than the least squares iterative algorithm.
