Parameter estimation with missing input/output data
Huazhen Fang,Yang Shi,Jian Wu +2 more
- 10 Jun 2009
- pp 5061-5066
TL;DR: A noise-robust minimum component analysis based algorithm is developed to recursively estimate parameters from the new ‘noisy’ input/output data and simulation results verify the effectiveness of the proposed algorithm.
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Abstract: The problem of recursive parameter estimation with missing input/output data is studied in this paper. A fictitious measurement noise model is presented for missing data, and a noise-robust minimum component analysis based algorithm is developed to recursively estimate parameters from the new ‘noisy’ input/output data. Convergence properties of the proposed algorithm are analyzed. The simulation results verify the effectiveness of the proposed algorithm.
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Citations
ImdLMS: An Imputation Based LMS Algorithm for Linear System Identification With Missing Input Data
TL;DR: The problem of linear system identification is studied with only input data missing at random time instant while output data is obtained correctly at all time instants while an LMS-type algorithm called Imputation based missing data LMS (ImdLMS) is proposed.
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•Dissertation
Consensus in multi-agent systems and bilateral teleoperation with communication constraints
Jian Wu
- 01 Jan 2013
TL;DR: This thesis is aimed to solve some problems in cooperative control involving multiple agents in the presence of communication constraints by designing appropriate control protocols such that the states of a group of agents will converge to a common value eventually.
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•Posted Content
On the MMSE Estimation of Norm of a Gaussian Vector under Additive White Gaussian Noise with Randomly Missing Input Entries
TL;DR: In this paper, the minimum mean square error (MMSE) estimator of the unknown Gaussian vector performing measurements under additive white Gaussian noise (AWGN) on the vector after the data missing was derived.
On the MMSE estimation of norm of a Gaussian vector under additive white Gaussian noise with randomly missing input entries
TL;DR: It is found that the corresponding MSE normalized by $n$ tends to 0 as $n\to \infty$ when $K/n$ is kept constant, and expressions for the MSE is derived when the variance of the AWGN noise tends to either $0$ or $\infty$.
Identification of dual-rate systems based on finite impulse response models
Feng Ding,Tongwen Chen +1 more
TL;DR: In this article, two identification algorithms, a least square and a correlation analysis based, are developed for dual-rate stochastic systems in which the output sampling period is an integer multiple of the input updating period.
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Combined parameter and output estimation of dual-rate systems using an auxiliary model
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Identification of dual-rate systems based on finite impulse response models
Feng Ding,Tongwen Chen +1 more
TL;DR: In this paper, two identification algorithms, a least square and a correlation analysis based, are developed for dual-rate stochastic systems in which the output sampling period is an integer multiple of the input updating period.
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