Special Issue "Parameter Estimation Algorithms and Its Applications"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Prof. Dr. Jing Na

Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology; Kunming, 650500, China
Department of Mechanical Engineering, University of Bristol, BS8 1TH Bristol, UK
Website | E-Mail
Interests: adaptive control; parameter estimation; nonlinear control and applications
Guest Editor
Prof. Dr. Feng Ding

School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Website | E-Mail
Phone: 15161598763
Interests: system identification; numerical algorithm; signal processing; system modeling
Guest Editor
Prof. Dr. Quan Min Zhu

FET - Engineering, Design and Mathematics, University of the West of England, Bristol, BS16 1QY UK
Website | E-Mail
Phone: +44 117 3282533
Interests: dynamic system modeling; identification; control and simulation

Special Issue Information

Dear Colleagues,

Many practical engineering problems can be addressed, provided that the precise models of the studied plants are known. This practically orientated need has stimulated the development of an emerging topic: System identification. As a key subject in the system identification, parameter estimation has been widely studied since 1960, and many algorithms have been used in the practice. However, there are still certain issues to be further revisited and addressed. This has stimulated recently increasing research interests and developments on advanced learning and adaptation for parameter estimation.

The open access journal Algorithms will host a Special Issue on “Parameter Estimation Algorithms and Its Applications”.

This Special Issue aims at providing a specific opportunity to review the state-of-the-art of Parameter Estimation Algorithms. Authors are invited to present new algorithms, frameworks, software architectures, experiments and applications aimed at bringing new information about relevant theory and techniques of parameter estimation. All original papers related to parameter estimation and their application are welcome. In particular, we encourage authors to introduce new results for synthesizing estimation and optimization into practical systems, e.g., multi-agent systems, smart grid, population systems, multi-agent systems, UAVs, human–robot interactions, etc.

Prof. Dr. Jing Na
Prof. Dr. Feng Ding
Prof. Dr. Quan Min Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 850 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Adaptive parameter estimation
  • System identification
  • Gradient algorithm
  • Least squares algorithm
  • Black-box identification
  • Grey-box identification
  • Bio-inspired learning and adaptation
  • Convergence and consistency
  • Kernel based identification

Published Papers (6 papers)

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Research

Open AccessArticle Parametric Estimation in the Vasicek-Type Model Driven by Sub-Fractional Brownian Motion
Algorithms 2018, 11(12), 197; https://doi.org/10.3390/a11120197
Received: 15 November 2018 / Revised: 30 November 2018 / Accepted: 30 November 2018 / Published: 4 December 2018
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Abstract
In the paper, we tackle the least squares estimators of the Vasicek-type model driven by sub-fractional Brownian motion: dXt=(μ+θXt)dt+dStH,t0 with X0
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In the paper, we tackle the least squares estimators of the Vasicek-type model driven by sub-fractional Brownian motion: d X t = ( μ + θ X t ) d t + d S t H , t 0 with X 0 = 0 , where S H is a sub-fractional Brownian motion whose Hurst index H is greater than 1 2 , and μ R , θ R + are two unknown parameters. Based on the so-called continuous observations, we suggest the least square estimators of μ and θ and discuss the consistency and asymptotic distributions of the two estimators. Full article
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
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Open AccessArticle Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model
Algorithms 2018, 11(11), 180; https://doi.org/10.3390/a11110180
Received: 30 September 2018 / Revised: 27 October 2018 / Accepted: 31 October 2018 / Published: 6 November 2018
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Abstract
This paper focuses on the joint estimation of parameters and time-delays of the multiple-input single-output output-error systems. Since the time-delays are unknown, an effective identification model with a high dimensional and sparse parameter vector is established based on overparameterization. Then, the identification problem
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This paper focuses on the joint estimation of parameters and time-delays of the multiple-input single-output output-error systems. Since the time-delays are unknown, an effective identification model with a high dimensional and sparse parameter vector is established based on overparameterization. Then, the identification problem is converted to a sparse optimization problem. Based on the basis pursuit de-noising criterion and the auxiliary model identification idea, an auxiliary model based basis pursuit de-noising iterative algorithm is presented. The parameters are estimated by solving a quadratic program, and the unavailable terms in the information vector are updated by the auxiliary model outputs iteratively. The time-delays are estimated according to the sparse structure of the parameter vector. The proposed method can obtain effective estimates of the parameters and time-delays from few sampled data. The simulation results illustrate the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
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Open AccessArticle The Bias Compensation Based Parameter and State Estimation for Observability Canonical State-Space Models with Colored Noise
Algorithms 2018, 11(11), 175; https://doi.org/10.3390/a11110175
Received: 4 September 2018 / Revised: 22 October 2018 / Accepted: 22 October 2018 / Published: 1 November 2018
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Abstract
This paper develops a bias compensation-based parameter and state estimation algorithm for the observability canonical state-space system corrupted by colored noise. The state-space system is transformed into a linear regressive model by eliminating the state variables. Based on the determination of the noise
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This paper develops a bias compensation-based parameter and state estimation algorithm for the observability canonical state-space system corrupted by colored noise. The state-space system is transformed into a linear regressive model by eliminating the state variables. Based on the determination of the noise variance and noise model, a bias correction term is added into the least squares estimate, and the system parameters and states are computed interactively. The proposed algorithm can generate the unbiased parameter estimate. Two illustrative examples are given to show the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
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Open AccessArticle Parameter Estimation of a Class of Neural Systems with Limit Cycles
Algorithms 2018, 11(11), 169; https://doi.org/10.3390/a11110169
Received: 11 September 2018 / Revised: 22 October 2018 / Accepted: 23 October 2018 / Published: 26 October 2018
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Abstract
This work addresses parameter estimation of a class of neural systems with limit cycles. An identification model is formulated based on the discretized neural model. To estimate the parameter vector in the identification model, the recursive least-squares and stochastic gradient algorithms including their
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This work addresses parameter estimation of a class of neural systems with limit cycles. An identification model is formulated based on the discretized neural model. To estimate the parameter vector in the identification model, the recursive least-squares and stochastic gradient algorithms including their multi-innovation versions by introducing an innovation vector are proposed. The simulation results of the FitzHugh–Nagumo model indicate that the proposed algorithms perform according to the expected effectiveness. Full article
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
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Open AccessArticle Online Adaptive Parameter Estimation for Quadrotors
Algorithms 2018, 11(11), 167; https://doi.org/10.3390/a11110167
Received: 11 September 2018 / Revised: 19 October 2018 / Accepted: 23 October 2018 / Published: 25 October 2018
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Abstract
The stability and robustness of quadrotors are always influenced by unknown or immeasurable system parameters. This paper proposes a novel adaptive parameter estimation technology to obtain high-accuracy parameter estimation for quadrotors. A typical mathematical model of quadrotors is first obtained, which can be
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The stability and robustness of quadrotors are always influenced by unknown or immeasurable system parameters. This paper proposes a novel adaptive parameter estimation technology to obtain high-accuracy parameter estimation for quadrotors. A typical mathematical model of quadrotors is first obtained, which can be used for parameter estimation. Then, an expression of the parameter estimation error is derived by introducing a set of auxiliary filtered variables. Moreover, an augmented matrix is constructed based on the obtained auxiliary filtered variables, which is then used to design new adaptive laws to achieve exponential convergence under the standard persistent excitation (PE) condition. Finally, a simulation and an experimental verification for a typical quadrotor system are shown to illustrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
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Open AccessArticle Estimating the Volume of the Solution Space of SMT(LIA) Constraints by a Flat Histogram Method
Algorithms 2018, 11(9), 142; https://doi.org/10.3390/a11090142
Received: 12 June 2018 / Revised: 12 September 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
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Abstract
The satisfiability modulo theories (SMT) problem is to decide the satisfiability of a logical formula with respect to a given background theory. This work studies the counting version of SMT with respect to linear integer arithmetic (LIA), termed SMT(LIA). Specifically, the purpose of
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The satisfiability modulo theories (SMT) problem is to decide the satisfiability of a logical formula with respect to a given background theory. This work studies the counting version of SMT with respect to linear integer arithmetic (LIA), termed SMT(LIA). Specifically, the purpose of this paper is to count the number of solutions (volume) of a SMT(LIA) formula, which has many important applications and is computationally hard. To solve the counting problem, an approximate method that employs a recent Markov Chain Monte Carlo (MCMC) sampling strategy called “flat histogram” is proposed. Furthermore, two refinement strategies are proposed for the sampling process and result in two algorithms, MCMC-Flat1/2 and MCMC-Flat1/t, respectively. In MCMC-Flat1/t, a pseudo sampling strategy is introduced to evaluate the flatness of histograms. Experimental results show that our MCMC-Flat1/t method can achieve good accuracy on both structured and random instances, and our MCMC-Flat1/2 is scalable for instances of convex bodies with up to 7 variables. Full article
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
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