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Partially coupled gradient estimation algorithm for multivariable equation-error autoregressive moving average systems using the data filtering technique

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IET CONTROL THEORY AND APPLICATIONS 2019年第5期13卷 642-650页

作者： Liu, Qinyao Ding, Feng Xu, Ling Yang, Erfu Jiangnan Univ Sch Internet Things Engn Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China King Abdulaziz Univ Dept Math Jeddah 21589 Saudi Arabia Univ Strathclyde Dept Design Manufacture & Engn Management Space Mechatron Syst Technol Lab Strathclyde Space Inst Glasgow G1 1XJ Lanark Scotland

System identification provides many convenient and useful methods for engineering modelling. This study targets the parameter identification problems for multivariable equation-error autoregressive moving average systems. To reduce the influence of the coloured noises on the parameter estimation, the data filtering technique is adopted to filter the input and output data, and to transform the original system into a filtered system with white noises. Then the filtered system is decomposed into several subsystems and a filtering-based partially-coupled generalised extended stochastic gradient algorithm is developed via the coupling concept. In contrast to the multivariable generalised extended stochastic gradient algorithm, the proposed algorithm can give more accurate parameter estimates. Finally, the effectiveness of the proposed algorithm is well demonstrated by simulation examples.

关键词： engineering modelling stochastic processes Other topics in statistics accurate parameter estimates useful methods output data white noises multivariable generalised extended stochastic gradient algorithm filtering-based autoregressive processes gradient methods white noise convenient methods average systems data filtering technique Interpolation and function approximation (numerical analysis) least squares approximations filtering methods in signal processing coloured noises input parameter identification problems multivariable equation-error autoregressive Simulation, modelling and identification autoregressive moving average processes filtered system parameter estimation system identification

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Parameter estimation for a multi-input multi-output state-space system with unmeasurable states through the data filtering technique

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IET CONTROL THEORY AND APPLICATIONS 2020年第19期14卷 3062-3075页

作者： Cui, Ting Ding, Feng Sheng, Jie Jiangnan Univ Sch Internet Things Engn Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China Univ Washington Sch Engn & Technol Tacoma WA USA

Parameter estimation is an important tool for modelling a real system. This study considers the parameter estimation problem of a multi-input multi-output state-space system with unmeasurable states. By employing the negative gradient search and cutting down redundant parameter estimates, the authors derive a partially-coupled generalised stochastic gradient (PC-GSG) algorithm to estimate the parameters. Considering the unmeasurable states, they present a new state observer which replaces the unknown parameters with their estimates to generate state estimates. By combining the PC-GSG algorithm and the new state observer, they obtain a state observer based partially-coupled generalised stochastic gradient (SO-PC-GSG) algorithm to estimate the parameters and states. In order to eliminate the interference of the coloured noise and strengthen the performance of the SO-PC-GSG algorithm, they propose a state observer based filtering PC-GSG algorithm by means of the data filtering technique. Finally, the effectiveness of the proposed algorithms is investigated in a simulation study.

关键词： control system synthesis observers stochastic systems filtering theory parameter estimation state-space methods gradient methods MIMO systems parameter estimation problem multiinput multioutput state-space system unmeasurable states redundant parameter estimates generalised stochastic gradient algorithm state observer unknown parameters SO-PC-GSG algorithm data filtering technique

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Parameter identification algorithm for ship manoeuvrability and wave peak model based multi-innovation stochastic gradient algorithm use data filtering technique

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DIGITAL SIGNAL PROCESSING 2024年 148卷

作者： Liu, Yang An, Shun Wang, Longjin He, Yan Fan, Zhimin Qingdao Univ Sci & Technol Coll Elect & Mech Engn Qingdao 266061 Peoples R China

This paper addresses the issue of identifying ship motion parameters and wave peak frequency. Utilising the Euler discretisation principle, we establish a discrete-time auto-regressive moving average model with exogenous input (ARMAX) for the ship-wave system. Furthermore, we develop a filtering-based stochastic gradient algorithm for the system by applying filtering techniques and auxiliary model identification idea. A filtering-based multiinnovation stochastic gradient algorithm, utilizing the multi-innovation identification theory, was developed to enhance the convergence rate and accuracy of parameter identification. This approach was found to be more effective than the filtering-based stochastic gradient algorithm. Simulation results validate the efficacy of the proposed algorithm in parameter identification.

关键词： Ship-wave discrete-time model Parameter model identification data filtering technique Multi-innovation identification theory Stochastic gradient algorithm

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Multi-innovation gradient estimation algorithms for multivariate equation-error autoregressive moving average systems based on the filtering technique

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IET CONTROL THEORY AND APPLICATIONS 2019年第13期13卷 2086-2094页

作者： Ma, Ping Ding, Feng Hayat, Tasawar Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Sch Internet Things Engn Wuxi 214122 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China King Abdulaziz Univ Dept Math Jeddah 21589 Saudi Arabia

This study concentrates on the parameter estimation of multivariate pseudo-linear autoregressive moving average systems by means of the multi-innovation identification theory and data filtering technique. A multi-innovation stochastic gradient algorithm is derived by introducing the innovation length in the stochastic gradient algorithm. Then, the original system is transformed into two subsystems by using a filter. A filtering-based multi-innovation stochastic gradient algorithm is presented, whose parameter estimation accuracy is higher than the multi-innovation stochastic gradient algorithm. The simulation results confirm that these two algorithms are effective.

关键词： gradient methods autoregressive moving average processes parameter estimation filtering theory multiinnovation gradient estimation algorithms multivariate equation-error autoregressive multivariate pseudolinear autoregressive moving average systems data filtering technique parameter estimation multiinnovation stochastic gradient algorithm

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A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems

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DIGITAL SIGNAL PROCESSING 2015年 46卷 215-225页

作者： Mao, Yawen Ding, Feng Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China Jiangnan Univ Control Sci & Engn Res Ctr Wuxi 214122 Peoples R China

The identification of nonlinear systems is a hot topic in the identification fields. In this paper, a data filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein nonlinear controlled autoregressive moving average systems by adopting the key-term separation principle and the data filtering technique. The proposed algorithm provides a reference to improve the identification accuracy of the nonlinear systems with colored noise. The simulation results show that the new algorithm can more effectively estimate the parameters of the Hammerstein nonlinear systems than the multi-innovation stochastic gradient algorithm. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Multi-innovation identification Nonlinear system Key-term separation principle data filtering technique

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data filtering-based parameter and state estimation algorithms for state-space systems disturbed by coloured noises

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2020年第9期51卷 1669-1684页

作者： Cui, Ting Ding, Feng Alsaedi, Ahmed Hayat, Tasawar Jiangnan Univ Sch Internet Things Engn Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China King Abdulaziz Univ Dept Math Jeddah Saudi Arabia

In this paper, the combined parameter and state estimation issues of state-space systems are considered, and the process noises and observation noises are supposed to be coloured noises. By utilising the data filtering technique, we transform the original state-space system into the filtered system for eliminating the interference of the coloured noise in the state equation, and then we derive a filtering-based extended stochastic gradient (F-ESG) algorithm to estimate the system parameters. For estimating the unmeasurable states, we derive a new state estimator by using the preceding parameter estimates to take the place of the unknown system parameters in the Kalman filter. Furthermore, we propose a filtering-based multi-innovation extended stochastic gradient (F-MI-ESG) algorithm to achieve the higher parameter estimation accuracy. Finally, we provide two simulation examples to test and compare the performance of the proposed algorithms. The simulation results indicate that the F-ESG algorithm and the F-MI-ESG algorithm are effective for parameter estimation, and that the F-MI-ESG algorithm is able to achieve more accurate parameter estimates than the F-ESG algorithm.

关键词： State-space system parameter estimation data filtering technique gradient search multi-innovation identification

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Iterative parameter identification for pseudo-linear systems with ARMA noise using the filtering technique

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IET CONTROL THEORY AND APPLICATIONS 2018年第7期12卷 892-899页

作者： Ding, Feng Xu, Ling Alsaadi, Fuad E. Hayat, Tasawar Jiangnan Univ Sch Internet Things Engn Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266042 Peoples R China King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia

This study considers the parameter identification problem of a pseudo-linear autoregressive moving average system (i.e. linear-in-parameter autoregressive output-error ARMA systems), whose disturbance is an ARMA process. By means of the filtering technique, a filtering-based gradient iterative (F-GI) algorithm and a filtering-based least squares iterative (LSI) algorithms are presented for enhancing the estimation accuracy. Furthermore, a filtering-based decomposition LSI algorithm is derived for improving the computational efficiency. The key is to use the hierarchical identification principle, to apply the data filtering technique for identification, and to replace the unknown terms in the information vectors with their estimates. Compared with the F-GI algorithm, the filtering-based LSI algorithm and the filtering-based decomposition LSI algorithm have faster convergence rates. The simulation results indicate that the proposed algorithms are effective.

关键词： linear systems autoregressive moving average processes filtering theory parameter estimation iterative methods gradient methods least squares approximations iterative parameter identification ARMA noise filtering technique pseudolinear autoregressive moving average system linear-in-parameter autoregressive output-error ARMA systems filtering-based gradient iterative algorithm F-GI algorithm filtering-based least squares iterative algorithm LSI algorithms hierarchical identification principle data filtering technique information vectors filtering-based decomposition LSI algorithm convergence rates

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data filtering-Based Multi-innovation Stochastic Gradient Algorithm for Nonlinear Output Error Autoregressive Systems

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2016年第2期35卷 651-667页

作者： Mao, Yawen Ding, Feng Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China

This paper discusses the parameter estimation problems of nonlinear output error autoregressive systems and presents a data filtering-based multi-innovation stochastic gradient algorithm for improving the parameter estimation accuracy of the stochastic gradient algorithm by combining the multi-innovation identification theory and the data filtering technique. The proposed algorithm is effective and can generate highly accurate parameter estimates compared with the multi-innovation stochastic gradient algorithm. The simulation results confirm this conclusion.

关键词： Stochastic gradient Multi-innovation identification theory data filtering technique Nonlinear system

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DEGRADATION OF DIFFERENT PHOTOVOLTAIC TECHNOLOGIES UNDER FIELD CONDITIONS

DEGRADATION OF DIFFERENT PHOTOVOLTAIC TECHNOLOGIES UNDER FIE...

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35th IEEE Photovoltaic Specialists Conference

作者： Makrides, George Zinsser, Bastian Georghiou, George E. Schubert, Markus Werner, Juergen H. Univ Cyprus Dept Elect & Comp Engn 75 Kallipoleos AvePOB 20537 CY-1678 Nicosia Cyprus Univ Stuttgart Inst Phys Elect D-70569 Stuttgart Germany

ISBN: (纸本)9781424458912

Over the past years a number of testing facilities have been monitoring the performance and degradation of PV systems according to the established standards of indoor and outdoor testing. The objective of this paper is to present the initial first year and longer-term rate of degradation of different PV technologies installed at the testing facility of the University of Cyprus, based on outdoor field measurements and methodologies. The first year degradation of the technologies was obtained using a data filtering technique of DC generated power at Maximum Power Point (MPP) at irradiation points of higher than 800 W/m(2) and normalising the measured power to Standard Test Conditions (STC). Over the first year, mono-crystalline silicon technologies showed degradations in the range 2.12 % - 4.73 % while for multi-crystalline technologies the range was 1.47 % - 2.40 %. The amorphous silicon system demonstrated the highest first year decrease in power with an average degradation of 13.82 %. For validation purposes the first year degradation was also obtained using a second technique by evaluating outdoor measured data-sets under Air Mass (AM) 1.5 (morning and afternoon) conditions and during noon (high irradiance and temperature). In this case the evaluated results showed deviations of up to 6 % and 3 % for monocrystalline and multi-crystalline technologies respectively whereas for thin-film this was 5 %. Finally, the longer-term degradation rates were evaluated by using the least-square fit method on average monthly data-set blocks of (i) Performance Ratio (PR), (ii) PR evaluated by filtering outage data-sets and restricting to high irradiance conditions and (iii) the Photovoltaic for Utility Systems Applications (PVUSA) rating methods, for the period June 2007 - June 2009.

关键词： DC generated power PV system amorphous semiconductors amorphous silicon system data filtering technique elemental semiconductors least square fit method least squares approximations maximum power point maximum power point trackers monocrystalline silicon technology multicrystalline technology outage data set blocks outdoor field measurements photovoltaic technology semiconductor thin films silicon solar cells standard test condition utility system application

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Separation identification approach for the Hammerstein-Wiener nonlinear systems with process noise using correlation analysis

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第14期33卷 8105-8123页

作者： Li, Feng Liang, Mingjun He, Naibao Cao, Qingfeng Jiangsu Univ Technol Coll Elect & Informat Engn Changzhou 213001 Peoples R China Yangzhou Univ Coll Elect Energy & Power Engn Yangzhou Peoples R China

This article develops a novel separation identification approach for the Hammerstein-Wiener nonlinear systems with process noise using correlation analysis technique. The Hammerstein-Wiener nonlinear systems have three parts, namely, an input nonlinear block, a linear block, and an output nonlinear block. The designed hybrid signals that consist of separable signal and random signal are devoted to achieving parameters separation identification of the Hammerstein-Wiener nonlinear system, that is, the three blocks are identified independently. First, the characteristics of separable signals under the action of static nonlinear block are analyzed, and two groups of separable signals with amplitude relation are utilized to estimate parameters of output nonlinear block. Moreover, the linear block parameters are identified by using correlation analysis approach, which deals with effectively immeasurable problem of internal variable information. Finally, the data filtering technique is implemented to weaken the influence of noises, and filtering-based recursive extended least squares algorithm is developed for figuring out the parameters of nonlinear block and colored noise model. The validity and accuracy of the proposed scheme are verified by two simulations, and simulation results exhibit that the proposed method can obtain higher identification precision and better robustness than the existing identification algorithms.

关键词： colored noise correlation analysis data filtering technique Hammerstein-wiener nonlinear systems separation identification

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