Journal ArticleDOI
Estimating time-varying parameters by the Kalman filter based algorithm: stability and convergence
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TLDR
In this article, the convergence and stability properties of the Kalman filter-based parameter estimator are established for linear stochastic time-varying regression models, where both the variances and sample path averages of the parameter tracking error are shown to be bounded.Abstract:
Convergence and stability properties of the Kalman filter-based parameter estimator are established for linear stochastic time-varying regression models. The main features are: both the variances and sample path averages of the parameter tracking error are shown to be bounded; the regression vector includes both stochastic and deterministic signals, and no assumptions of stationarity or independence are requires; and the unknown parameters are only assumed to have bounded variations in an average sense. >read more
Citations
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Journal ArticleDOI
Stochastic stability of the discrete-time extended Kalman filter
TL;DR: It is shown that the estimation error remains bounded if the system satisfies the nonlinear observability rank condition and the initial estimation error as well as the disturbing noise terms are small enough.
Journal ArticleDOI
A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning
Chia-Feng Juang,Yu-Wei Tsao +1 more
TL;DR: This paper proposes a self-evolving interval type-2 fuzzy neural network (SEIT2FNN) with online structure and parameter learning, which is applied to simulations on nonlinear plant modeling, adaptive noise cancellation, and chaotic signal prediction.
Journal ArticleDOI
Kalman filter-based identification for systems with randomly missing measurements in a network environment
Yang Shi,Huazhen Fang +1 more
TL;DR: This work model the input and output missing data as two separate Bernoulli processes characterised by probabilities of missing data, then a missing output estimator is designed, and a recursive algorithm for parameter estimation is developed by modifying the Kalman filter-based algorithm.
Journal ArticleDOI
Stochastic optimal control of unknown linear networked control system in the presence of random delays and packet losses
TL;DR: The proposed stochastic optimal control method uses an adaptive estimator (AE) and ideas from Q-learning to solve the infinite horizon optimal regulation of unknown NCS with time-varying system matrices and produces an optimal control scheme that operates forward-in-time manner for unknown linear systems.
Journal ArticleDOI
Computer control under time-varying sampling period: An LMI gridding approach
TL;DR: This paper addresses computer control under time-varying sampling period and delayed actuation by means of linear matrix inequalities (LMI) and quadratic Lyapunov functions and shows the applicability of the approach in a real implementation.
References
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Book ChapterDOI
Stationary and nonstationary learning characteristics of the LMS adaptive filter
TL;DR: It is shown that for stationary inputs the LMS adaptive algorithm, based on the method of steepest descent, approaches the theoretical limit of efficiency in terms of misadjustment and speed of adaptation when the eigenvalues of the input correlation matrix are equal or close in value.
Journal ArticleDOI
Adaptation and tracking in system identification—a survey
Lenhart Ljung,Svante Gunnarsson +1 more
TL;DR: This article gives a survey of basic techniques to derive and analyse algorithms for tracking time-varying systems, with special attention to the study of how different assumptions about the true system's variations affect the algorithm.
Journal ArticleDOI
A smoothness priors time-varying AR coefficient modeling of nonstationary covariance time series
Genshiro Kitagawa,Will Gersch +1 more
TL;DR: In this article, a smoothness priors time varying AR coefficient model approach for the modeling of nonstationary in the covariance time series is presented, where the unknown white noise variances are hyperparameters of the AR coefficient distribution.