Next Article in Journal
Towards the Verbal Decision Analysis Paradigm for Implementable Prioritization of Software Requirements
Next Article in Special Issue
Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model
Previous Article in Journal
Local Coupled Extreme Learning Machine Based on Particle Swarm Optimization
Previous Article in Special Issue
Parameter Estimation of a Class of Neural Systems with Limit Cycles
Article Menu

Export Article

Open AccessArticle
Algorithms 2018, 11(11), 175; https://doi.org/10.3390/a11110175

The Bias Compensation Based Parameter and State Estimation for Observability Canonical State-Space Models with Colored Noise

1
College of Mathematics and Statistics, Xinyang Normal University, Xinyang 464000, China
2
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
*
Author to whom correspondence should be addressed.
Received: 4 September 2018 / Revised: 22 October 2018 / Accepted: 22 October 2018 / Published: 1 November 2018
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
Full-Text   |   PDF [698 KB, uploaded 2 November 2018]   |  

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 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. View Full-Text
Keywords: recursive identification; least squares; bias compensation; state-space model; state estimation recursive identification; least squares; bias compensation; state-space model; state estimation
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wang, X.; Ding, F.; Liu, Q.; Jiang, C. The Bias Compensation Based Parameter and State Estimation for Observability Canonical State-Space Models with Colored Noise. Algorithms 2018, 11, 175.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top