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Identification of a nonlinear rational model based on bias compensated multi-innovation stochastic gradient algorithm

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AUTOMATIKA 2022年第4期63卷 785-792页

作者： Jing, S. X. Liu, R. R. Chen, L. Huaiyin Normal Univ Sch Phys & Elect Elect Engn 111 Changjiang West Rd Huaian 223300 Jiangsu Peoples R China Jiangsu Univ Technol Sch Automot & Traff Engn Changzhou Peoples R China

The nonlinear rational model is a generalized nonlinear model and has been gradually applied in modelling many dynamic processes. The parameter identification of a class of nonlinear rational models is studied in this paper. This identification problem is very challenging because of the complexity of the rational model and the coupling between model inputs and outputs. To identify the nonlinear model, a bias compensated multi-innovation stochastic gradient algorithm is presented. The multi-innovation technique replacing the scalar innovation with an information vector is adopted to accelerate the traditional stochastic gradient algorithm. However, the estimate obtained by the accelerated algorithm is biased because of the correlation between the information vector and the noise. To overcome this difficulty, a bias compensation strategy is used. The bias is calculated and compensated to get an unbiased estimate. Theoretical analysis shows that the proposed algorithm can give biased estimates with linear complexity. The proposed algorithm is validated by a numerical experiment and the modelling of the propylene catalytic oxidation.

关键词： Nonlinear rational model parameter estimation adaptive modelling bias compensation stochastic gradient algorithm

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Generalized Extended stochastic gradient algorithm Implemented Parameter Identification for Complex Multivariable-Systems 11th

Generalized Extended Stochastic Gradient Algorithm Implement...

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11th International Conference on Modelling, Identification and Control (ICMIC)

作者： Wang, Wei Zaozhuang Vocat Coll Econ & Informat Technol Dept Zaozhuang 277000 Shandong Peoples R China

ISBN: (纸本)9789811504747;9789811504730

In this paper, a new recognition method is deduced based on the theory of model equivalence in order to modify the parameter estimation for the multi-input nonlinear equation-error autoregressive moving average(Multi-variable) system. Using the theory of model equivalence, using the auxiliary model to handle the colored noise, the proposed algorithm reduces the number of unknown noise items in the recognition model information vector and achieves better recognition accuracy. For comparison, we use the recursive generalized extended least squares (RGELS) algorithm. To confirm the effectiveness of the algorithm, an example is shown.

关键词： Multi-variable system stochastic gradient algorithm Model equivalence Parameter estimation

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The filtering based multi-innovation stochastic gradient algorithm for multi-input output-error systems 36

The filtering based multi-innovation stochastic gradient alg...

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第36届中国控制会议

作者： Jiling Ding Department of Mathematics Jining University

ISBN: (纸本)9781538629185

In this paper, we discuss the identification problem of multi-input output-error moving average systems. A filtering based multi-innovation stochastic gradient algorithm with forgetting factors is derived by using the filtering technique with the multi-innovation identification theory, and a multi-innovation extended stochastic gradient algorithm is given for *** simulation results confirm that the proposed algorithms can generate highly accurate parameter estimates compared with the multi-innovation stochastic gradient algorithm.

关键词： Multi-input output system Multi-innovation stochastic gradient algorithm Filtering technique Parameter estimation

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A joint stochastic gradient algorithm and its application to system identification with RBF networks

A joint stochastic gradient algorithm and its application to...

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6th World Congress on Intelligent Control and Automation

作者： Chen, Badong Hu, Jinchun Li, Hongbo Sun, Zengqi Tsing Hua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China

ISBN: (纸本)1424403316

Mean-square-error (MSE) and minimum-error-entropy (MEE) criteria play significant roles in adaptive filtering and learning theory. Nevertheless, both the criteria have their respective shortcomings. In this paper, we propose a more general and effective stochastic gradient algorithm under joint criterion of MSE and MEE, and derive the approximate upper bound for the step size in the adaptive linear neuron (ADALINE) training. In particular, we demonstrate the superiority of this joint adaptive algorithm by applying it into system identification with radial basis function (RBF) networks.

关键词： stochastic gradient algorithm MSE MEE ADALINE RBF networks

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An Improved stochastic gradient algorithm to Identify PMSM Parameters Based on CAR Models 36

An Improved Stochastic Gradient Algorithm to Identify PMSM P...

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第36届中国控制会议

作者： Hui-jie Sun Rui-Qi Dong Ying Zhang Harbin Institute of Technology Shenzhen Graduate School

ISBN: (纸本)9781538629185

In this paper, we study the parameters identification problem of Permanent Magnet Synchronous Motor(PMSM) in steady state. First, the controlled auto-regressive(CAR) model of PMSM is established. Secondly, based on the obtained CAR model, an improved stochastic gradient algorithm is proposed to identify the electrical parameters of PMSM. By introducing a tuning parameter in the presented algorithm, the current estimation for the unknown PMSM parameters is updated by using the information not only in the current step but also in the previous step. In addition, a convergence result is provided for the developed algorithm. Finally, an example is given to show the advantage of the proposed algorithm for the parameters identification of PMSM.

关键词： Parameter Identification Convergence Analysis PMSM stochastic gradient algorithm

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stochastic gradient algorithm for state space system with d-step delay

Stochastic gradient algorithm for state space system with d-...

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第26届中国控制与决策会议

作者： Ya Gu Rui Ding Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University School of Internet of Things Engineering Jiangnan University

This paper researches parameter and state estimation problems for linear systems with d-step delay. Combining the linear transformation and the property of the shift operator, the canonical state space model with d-step delay is transformed into an identification model. The stochastic gradient algorithm is raised to identify the parameter ***, an example is presented to validate the given algorithms.

关键词： stochastic gradient algorithm State space models Time-delay

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A Fast stochastic gradient algorithm: Maximal Use of Sparsification Benefits under Computational Constraints

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2010年第2期E93A卷 467-475页

作者： Yukawa, Masahiro Utschick, Wolfgang RIKEN BSI Lab Math Neurosci Wako Saitama 3510198 Japan Tech Univ Munich Associate Inst Signal Proc D-80290 Munich Germany

In this paper, we propose a novel stochastic gradient algorithm for efficient adaptive filtering. The basic idea is to sparsity the initial error vector and maximize the benefits from the sparsification under computational constraints. To this end, we formulate the task of algorithm-design as a constrained optimization problem and derive its (non-trivial) closed-form solution. The computational constraints, are formed by focusing on the fact that the energy of the sparsified error vector concentrates at the first few components. The numerical examples demonstrate that the proposed algorithm achieves the convergence as fast as the computationally expensive method based on the optimization without the computational constraints.

关键词： adaptive filter stochastic gradient algorithm proportionate adaptive filtering

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Online distribution system state estimation via stochastic gradient algorithm*

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ELECTRIC POWER SYSTEMS RESEARCH 2022年 213卷

作者： Huang, Jianqiao Zhou, Xinyang Cui, Bai IIT Dept Elect & Comp Engn Chicago IL USA Natl Renewable Energy Lab Power Syst Engn Ctr Golden CO 80401 USA

Distribution network operation is becoming more challenging because of the growing integration of intermit-tent and volatile distributed energy resources (DERs). This motivates the development of new distribution system state estimation (DSSE) paradigms that can operate at fast timescale based on real-time data stream of asynchronous measurements enabled by modern information and communications technology. To solve the real-time DSSE with asynchronous measurements effectively and accurately, this paper formulates a weighted least squares DSSE problem and proposes an online stochastic gradient algorithm to solve it. The performance of the proposed scheme is analytically guaranteed and is numerically corroborated with realistic data on IEEE 123-bus feeder.

关键词： Distribution system state estimation Online asynchronous update stochastic gradient algorithm

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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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Global Mapping Analysis: stochastic gradient algorithm in Multidimensional Scaling

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2012年第2期E95D卷 596-603页

作者： Matsuda, Yoshitatsu Yamaguchi, Kazunori Aoyama Gakuin Univ Sagamihara Kanagawa 2525258 Japan Univ Tokyo Tokyo 1538902 Japan

In order to implement multidimensional scaling (MDS) efficiently, we propose a new method named "global mapping analysis" (GMA), which applies stochastic approximation to minimizing MDS criteria. GMA can solve MDS more efficiently in both the linear case (classical MDS) and non-linear one (e.g., ALSCAL) if only the MDS criteria are polynomial. GMA separates the polynomial criteria into the local factors and the global ones. Because the global factors need to be calculated only once in each iteration, GMA is of linear order in the number of objects. Numerical experiments on artificial data verify the efficiency of GMA. It is also shown that GMA can find out various interesting structures from massive document collections.

关键词： multidimensional scaling stochastic gradient algorithm text mining

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