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43rd Chinese Control Conference (CCC)

作者： Zhang, Xiufeng Tian, Zihan Zhang, Anqi Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

The mathematical model of ship maneuvering motion is the core of ship motion simulation and control, and it is still a difficult problem to accurately establish the mathematical model that reflects the actual ship motion state. In this paper, based on the typical maneuvering test data of the real ship, the recursive prediction error (RPE) method is used to identify the parameters of the 4-DOF motion mathematical model of the real ship, and the grey box mathematical model driven by the real ship data is established. Compared with the experimental data outside the training sample data set, the results have higher accuracy and reliability. This method is suitable for the motion prediction of real ship based on typical test data and can be used in the modeling of intelligent ship motion control and autonomous navigation motion prediction mathematical model.

关键词： Parameter identification Ship motion predict Typical ship sea trial recursive prediction error method full-scale YUKUN

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A Low-Complexity DPD to Fully Linearize the Power Amplifiers in a mMIMO Transmitter 59

A Low-Complexity DPD to Fully Linearize the Power Amplifiers...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Prasad, Ganesh Johansson, Hakan Laskar, Rabul Hussain Linkoping Univ Dept Elect Engn Div Commun Syst Linkoping Sweden Natl Inst Technol Silchar Dept Elect & Commun Engn Silchar India

ISBN: (纸本)9781728190549

A radio frequency (RF) power amplifier (PA) is crucial to enhance the signal to transmit via antenna over long distances. High-power transmission often leads to nonlinear behavior in the PA, necessitating the use of digital predistortion (DPD) signal processing to restore linearity by preinverting the nonlinearity. However, when dealing with a massive MIMO (mMIMO) transmitter with numerous PAs, a single DPD is not enough, and allocating a separate DPD for each PA is intricate and cost-inefficient. In this study, we tackle these challenges through our proposed low-complexity DPD (LC-DPD) architecture. The LC-DPD has the flexibility to choose the parameters of its architecture as per the desired tradeoff between the performance and complexity in the linearization. It employs learning of its coefficients through algorithms utilizing an indirect learning architecture based recursive prediction error method (ILA-RPEM), which is adaptive and free from matrix inversions.

关键词： Digital predistortion signal processing indirect learning architecture recursive prediction error method

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Application of RPE in Ship Maneuvering Motion Parameter Identification Based on Ship Sea Trial Data

Application of RPE in Ship Maneuvering Motion Parameter Iden...

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第43届中国控制会议

作者： Xiufeng Zhang Zihan Tian Anqi Zhang Navigation College Dalian Maritime University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The mathematical model of ship maneuvering motion is the core of ship motion simulation and control, and it is still a difficult problem to accurately establish the mathematical model that reflects the actual ship motion state. In this paper, based on the typical maneuvering test data of the real ship, the recursive prediction error(RPE) method is used to identify the parameters of the 4-DOF motion mathematical model of the real ship, and the grey box mathematical model driven by the real ship data is established. Compared with the experimental data outside the training sample data set, the results have higher accuracy and reliability. This method is suitable for the motion prediction of real ship based on typical test data and can be used in the modeling of intelligent ship motion control and autonomous navigation motion prediction mathematical model.

关键词： Parameter identification Ship motion predict Typical ship sea trial recursive prediction error method full-scale YUKUN

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Particle filtering-based recursive identification for controlled auto-regressive systems with quantised output

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IET CONTROL THEORY AND APPLICATIONS 2019年第14期13卷 2181-2187页

作者： Ding, Jie Chen, Jiazhong Lin, Jinxing Jiang, Guoping Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Jiangsu Engn Lab IOT Intelligent Robots Nanjing 210023 Jiangsu Peoples R China

recursive prediction error method is one of the main tools for analysis of controlled auto-regressive systems with quantised output. In this study, a recursive identification algorithm is proposed based on the auxiliary model principle by modifying the standard stochastic gradient algorithm. To improve the convergence performance of the algorithm, a particle filtering technique, which approximates the posterior probability density function with a weighted set of discrete random sampling points is utilised to correct the linear output estimates. It can exclude those invalid particles according to their corresponding weights. The performance of the particle filtering technique-based algorithm is much better than that of the auxiliary model-based one. Finally, results are verified by examples from simulation and engineering.

关键词： stochastic processes parameter estimation least squares approximations filtering theory gradient methods autoregressive processes recursive estimation probability particle filtering (numerical methods) auxiliary model principle standard stochastic gradient algorithm novel particle filtering technique posterior probability density function discrete random sampling points linear output estimates invalid particles particle filtering technique-based algorithm auxiliary model-based particle filtering-based recursive identification controlled auto-regressive systems quantised output recursive prediction error method main tools novel recursive identification algorithm

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Adaptive Hammerstein Predistorter Using the recursive prediction error method

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Tsinghua Science and Technology 2008年第1期13卷 17-22页

作者：李辉王德生陈兆武 Department of Electronic Engineering Tsinghua University

The digital baseband predistorter is an effective technique to compensate for the nonlinearity of power amplifiers （PAs） with memory effects. However, most available adaptive predistorters based on direct learning architectures suffer from slow convergence speeds. In this paper, the recursive prediction error method is used to construct an adaptive Hammerstein predistorter based on the direct learning architecture, which is used to linearize the Wiener PA model. The effectiveness of the scheme is demonstrated on a digital video broadcasting-terrestrial system. Simulation results show that the predistorter outperforms previous predistorters based on direct learning architectures in terms of convergence speed and linearization. A similar algorithm can be applied to estimate the Wiener PA model, which will achieve high model accuracy.

关键词： power amplifier predistorter Wiener system Hammerstein system recursive prediction error method

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Auxiliary model based parameter estimation for dual-rate output error systems with colored noise

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APPLIED MATHEMATICAL MODELLING 2013年第6期37卷 4051-4058页

作者： Ding, Jie Fan, Chunxia Lin, Jinxing Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210046 Peoples R China

The dual-rate sampled-data systems can offer better quality of control than the systems with single sampling rate in practice. However, the conventional identification methods run in the single-rate scheme. This paper focuses on the parameter estimation problems of the dual-rate output error systems with colored noises. Based on the dual-rate sampled and noise-contaminated data, two direct estimation algorithms are addressed: the auxiliary model based recursive extended least squares algorithm and the recursive prediction error method. The auxiliary model is employed to estimate the noise-free system output. An example is given to test and illustrate the proposed algorithms. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Dual-rate sampled data Auxiliary model Output error model Least square recursive prediction error method

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recursive online EM estimation of mixture autoregressions

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2013年第2期83卷 370-383页

作者： Aknouche, Abdelhakim Univ Sci & Technol Houari Boumedienne Fac Math Algiers Algeria Qassim Univ Dept Math Qasim Saudi Arabia

This paper develops a recursive expectationmaximization (REM) algorithm for estimating a mixture autoregression (MAR) with an independent and identically distributed regime transition process. The proposed method, which is useful for long time series as well as for data available in real time, follows a recursive predictor error-type scheme. Based on a slightly modified system to the expectationmaximization (EM) equations for an MAR model, the REM algorithm consists of two steps at each iteration: the expectation step, in which the current unobserved regime transition is estimated from new data using previous recursive estimates, and the minimization step, in which the MAR parameter estimates are recursively updated following a minimization direction. Details of implementation of the REM algorithm are given and its finite-sample performance is shown via simulation experiments. In particular, the EM and REM provide roughly similar estimates, especially for moderate and long time series.

关键词： mixture autoregression recursive prediction error method recursive weighted least squares EM algorithm recursive EM algorithm AMS 2000 Primary 62M10 Secondary 60F99

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Parameter Estimation for OEMA ModelsWith Dual-Rate Input-Output Data

Parameter Estimation for OEMA ModelsWith Dual-Rate Input-Out...

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31st Chinese Control Conference

作者： Ding Jie Yin Jun Zhu Songhao Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210003 Jiangsu Peoples R China Donghua Univ Sch Informat Sci & Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9789881563811

This paper deals with the parameter estimation problems of the dual-rate sampled-data systems. The output error models with colored noise are considered as the nature models for systems in industries and thus are taken to model the dual-rate sampled-data systems in this paper. The recursive prediction error method is proposed to estimate the parameters directly from the dual-rate sampled data, and an auxiliary model is employed to estimate the noise-free system output. Simulation is given to test and illustrate the proposed algorithm in the paper.

关键词： Output error Model Dual-Rate Sampled Data Auxiliary Model recursive prediction error method

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Parameter Estimation for OEMA Models With Dual-Rate Input-Output Data

Parameter Estimation for OEMA Models With Dual-Rate Input-Ou...

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第三十一届中国控制会议

作者： DING Jie~1,YIN Jun~2,ZHU Songhao~1 1.College of Automation,Nanjing University of Posts & Telecommunications,Nanjing 210003,P.R.China 2.School of Information Science & Technology,Donghua University,Shanghai,201620,P.R.China

This paper deals with the parameter estimation problems of the dual-rate sampled-data *** output error models with colored noise are considered as the nature models for systems in industries and thus are taken to model the dual-rate sampled-data systems in this *** recursive prediction error method is proposed to estimate the parameters directly from the dual-rate sampled data,and an auxiliary model is employed to estimate the noise-free system *** is given to test and illustrate the proposed algorithm in the paper.

关键词： Output error Model Dual-Rate Sampled Data Auxiliary Model recursive prediction error method

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Model-based estimation of the torque characteristics of an automated dual-clutch transmission in wet technology

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IFAC Proceedings Volumes 2010年第7期43卷 455-460页

作者： A. Tarasow C. Bohn M. Vinaske G. Wachsmuth R. Serway Institute of Electrical Information Technology Clausthal University of Technology Clausthal-Zellerfeld Germany IAV GmbH Ingenieurgesellschaft Auto und Verkehr Berlin Germany

In many modern automotive driveline concepts the power transfer between the drive input and output is conducted by a friction clutch. The drivability of a vehicle and the exploitation of the engine power depend primarily on the interaction between the available torque and the torque delivered through the clutch. The clutch characteristic is therefore important in this context. A clutch characteristic defines the relationship between the actuating variable of the contact force and the torque delivered through the clutch. In this paper two different model approaches are discussed for estimating of the clutch characteristics using an adaptive, extended Kalman filter in combination with the recursive prediction error method. The two approaches differ in the level of abstraction and itemization used in the prediction models. The verification studies indicate that both modelling approaches allow an unbiased estimation of the clutch characteristic. However, model-dependent differences arise in transient behaviour (such as convergence time) as well as in the implementation and computational costs.

关键词： adaptive Kalman filter parameter estimation recursive prediction error method clutch control dual-clutch transmission automotive driveline control

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