Gradient-based and least-squares-based iterative estimation algorithms for multi-input multi-output systems

Ding, Feng; Liu, Yanjun; Bao, Bo

doi:10.1177/0959651811409491

Cited by 160 publications

(115 citation statements)

References 43 publications

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“…For example, multi-innovation stochastic gradient [3] and hierarchical least squares algorithms [4] have been proposed for multi-output systems. Gradient-based and least-squares-based iterative estimation algorithms for MIMO systems have also been proposed [5]. Integrating support vector regression and annealing dynamical learning algorithm, a robust approach was developed to optimize a radial basis function (RBF) network for the identification of MIMO systems [1].…”

Section: Introductionmentioning

confidence: 99%

A multi-output two-stage locally regularized model construction method using the extreme learning machine

et al. 2014

Neurocomputing

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. (2014). A multi-output two-stage locally regularized model construction method using the extreme learning machine. Neurocomputing, 128, 104-112. DOI: 10.1016/j.neucom.2013 Published in: Neurocomputing Document Version: Peer reviewed version Queen's University Belfast -Research Portal: Link to publication record in Queen's University Belfast Research Portal Publisher rights NOTICE: this is the author's version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Vo. 128, 2014 General rights Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.Take down policy The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact openaccess@qub.ac.uk. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractThis paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multioutput two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor i...

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Section: Introductionmentioning

confidence: 99%

A multi-output two-stage locally regularized model construction method using the extreme learning machine

et al. 2014

Neurocomputing

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“…Iterative algorithms or recursive algorithms can be used for parameter estimation and state filtering and for solving matrix equations [3,9,29,42]. Classic iterative algorithms are the Newton iteration, the Jacobi iteration and the Gauss-Seidel iteration for solving equations Ax = b; classic recursive algorithms contain the Kalman filtering for the state-space systems.…”

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Performance Analysis of the Auxiliary Model-Based Stochastic Gradient Parameter Estimation Algorithm for State-Space Systems with One-Step State Delay

Ding

2012

Circuits Syst Signal Process

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How to use the observation data to build the mathematical models of timedelay systems and how to estimate the parameters of the obtained models are important for studying the laws of motion of systems. This paper presents an auxiliary model-based stochastic gradient parameter estimation algorithm and studies its convergence for the input-output representation for state-space systems with one-step delays, by means of the auxiliary model identification idea. The simulation results indicate that the proposed algorithm can effectively estimate the parameters of the systems.

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“…In this paper, we re-visit the Kalman filter, but address many of the concerns above, identifying a MIMO model of unknown structure and minimum parameter set using an iterative time-domain approach. One existing publication (Ding et al, 2012) operates in a similar way to that considered here, except that it employs a recursive least-squares method and assumes a discrete model of known order. In the new method, the most appropriate model order is also identified, and we also see that the same Identifying Extended Kalman Filter (IEKF) can be configured to identify both linear and nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Extending the Kalman filter for structured identification of linear and nonlinear systems

Best

Bogdanski

2017

IJMIC

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This paper considers a novel approach to system identification which allows accurate models to be created for both linear and nonlinear multi-input/output systems. In addition to conventional system identification applications, the method can also be used as a black-box tool for model order reduction. A nonlinear Kalman filter is extended to include slow-varying parameter states in a canonical model structure. Interestingly, in spite of all model parameters being unknown at the start, the filter is able to evolve parameter estimates to achieve 100% accuracy in noise-free test cases, and is also proven to be robust to noise in the measurements. The canonical structure ensures a well-conditioned model which simultaneously provides valuable dynamic information to the engineer. After extensive testing of a linear example, the model structure is extended to a generalised nonlinear form, which is shown to accurately identify the handling response of a full vehicle model.

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Gradient-based and least-squares-based iterative estimation algorithms for multi-input multi-output systems

Cited by 160 publications

References 43 publications

A multi-output two-stage locally regularized model construction method using the extreme learning machine

A multi-output two-stage locally regularized model construction method using the extreme learning machine

Performance Analysis of the Auxiliary Model-Based Stochastic Gradient Parameter Estimation Algorithm for State-Space Systems with One-Step State Delay

Extending the Kalman filter for structured identification of linear and nonlinear systems

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