Two-stage least squares based iterative estimation algorithm for CARARMA system modeling ☆
TLDR
In this article, a two-stage least squares based iterative algorithm is proposed for identifying the system model parameters and the noise model parameters for stochastic systems described by CARARMA models.About:
This article is published in Applied Mathematical Modelling.The article was published on 2013-04-01 and is currently open access. It has received 171 citations till now. The article focuses on the topics: Non-linear least squares & Autoregressive–moving-average model.read more
Citations
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Journal ArticleDOI
The damping iterative parameter identification method for dynamical systems based on the sine signal measurement
TL;DR: A damping parameter estimation algorithm for dynamical systems based on the sine frequency response is proposed and a damping factor is introduced in the proposed iterative algorithm in order to overcome the singular or ill-conditioned matrix during the iterative process.
Journal ArticleDOI
Coupled-least-squares identification for multivariable systems
TL;DR: In this article, a coupled-least-squares (C-LS) parameter identification algorithm is introduced for the purpose of avoiding the matrix inversion in the multivariable recursive least squares (RLS) algorithm for estimating the parameters of the multiple linear regression models.
Journal ArticleDOI
Combined state and least squares parameter estimation algorithms for dynamic systems
TL;DR: The parameter estimation algorithm of establishing the mathematical models for dynamic systems is discussed and an estimated states based recursive least squares algorithm is presented, and the states of the system are computed through the Kalman filter using the estimated parameters.
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Control Algorithms of Magnetic Suspension Systems Based on the Improved Double Exponential Reaching Law of Sliding Mode Control
Jian Pan,Wei Li,Haipeng Zhang +2 more
TL;DR: The improved algorithm has better control performances than the traditional SMC and the power reaching law integral SMC algorithm, such as less chattering, smaller overshoots, and faster response speed.
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State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors
TL;DR: This paper proposes a state filtering method for the single-input–single-output bilinear systems by minimizing the covariance matrix of the state estimation errors by extending the extended Kalman filter algorithm to multiple- input–multiple-output Bilinear Systems.
References
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TL;DR: This paper is concerned with the two-mode-dependent robust control synthesis of networked control systems where random delays existing in both forward controller-to-actuator (C-A) and feedback sensor- to-controller (S-C) communication links are modeled as Markov chains.
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Identification methods for Hammerstein nonlinear systems
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Performance analysis of multi-innovation gradient type identification methods
Feng Ding,Tongwen Chen +1 more
TL;DR: The performance analysis and simulation results show that the proposed MISG and MIFG algorithms have faster convergence rates and better tracking performance than their corresponding SG algorithms.
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Hierarchical least squares identification methods for multivariable systems
Feng Ding,Tongwen Chen +1 more
TL;DR: For multivariable discrete-time systems described by transfer matrices, an HLSI algorithm and a hierarchical least squares iterative algorithm based on a hierarchical identification principle are developed and shown to have significant computational advantage over existing identification algorithms.
Journal ArticleDOI
Partially Coupled Stochastic Gradient Identification Methods for Non-Uniformly Sampled Systems
TL;DR: The analysis indicates that the partially C-SG algorithm can give more accurate parameter estimates than the standard stochastic gradient (SG) algorithm.
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