Identification of Linear Models From Quantized Data: A Midpoint-Projection Approach

Risuleo, Riccardo Sven; Bottegal, Giulio; Hjalmarsson, Håkan

doi:10.1109/tac.2019.2933134

Cited by 15 publications

(7 citation statements)

References 35 publications

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“…In the literature of quantized identification, there are multiple methods not relying on the design of inputs or quantizers. For example, the maximum likelihood method was used in [1], [19], [29], [37]. An online algorithm based on the expectation-maximization (EM) algorithm and quasi-Newton method for autoregressive moving average (ARMA) models with quantized outputs was studied in [29].…”

Section: B Related Workmentioning

confidence: 99%

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Network Weight Estimation for Binary-Valued Observation Models

Fang

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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This paper studies the problem of recursively estimating the weighted adjacency matrix of a network out of a temporal sequence of binary-valued observations. The observation sequence is generated from nonlinear networked dynamics in which agents exchange and display binary outputs. Sufficient conditions are given to ensure stability of the observation sequence and identifiability of the system parameters. It is shown that stability and identifiability can be guaranteed under the assumption of independent standard Gaussian disturbances. Via a maximum likelihood approach, the estimation problem is transformed into an optimization problem, and it is verified that its solution is the true parameter vector under the independent standard Gaussian assumption. A recursive algorithm for the estimation problem is then proposed based on stochastic approximation techniques. Its strong consistency is established and convergence rate analyzed. Finally, numerical simulations are conducted to illustrate the results and to show that the proposed algorithm is insensitive to small unmodeled factors.

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Section: B Related Workmentioning

confidence: 99%

“…To achieve the best performance, quantizers need to be known and adaptive. In [1], [19], the EM algorithm was used to optimize the likelihood function, while in [37] a variational approximation approach was utilized. Additionally, Bayesian frameworks were applied in, e.g., [9].…”

Section: B Related Workmentioning

confidence: 99%

Network Weight Estimation for Binary-Valued Observation Models

Fang

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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“…Infinite-level quantization has been studied in control systems [18], system identification [25,42], communications [51], etc. Although practical quantizers have a finite number of levels, in the case of input signals with unbounded support they are difficult to analyse, because of the unbounded overload regions.…”

Section: Finite Resolution Quantizationmentioning

confidence: 99%

Granger Causality of Gaussian Signals from Quantized Measurements

Ahmadi

Nair

Weyer

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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An approach is proposed for inferring Granger causality between jointly stationary, Gaussian signals from quantized data. First, a necessary and sufficient rank criterion for the equality of two conditional Gaussian distributions is proved. Assuming a partial finite-order Markov property, sufficient conditions are then derived under which Granger causality between them can be reliably inferred from the second order moments of the quantized processes. This approach does not require the statistics of the underlying Gaussian signals to be estimated, or a system model to be identified.

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“…Based on the Gauss-Markov theorem, a quantile-based estimator was proposed in [8]. More research about system identification using quantized data can be seen in [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Identification of Rational Systems with Logarithmic Quantized Data

2021

Mathematical Problems in Engineering

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This paper is concerned with the quantized identification of rational systems, where the systems’ output is quantized by a logarithmic quantizer. Under the assumption that the systems’ input is periodic, the identification procedure is categorized into two steps. The first step is to identify the noise-free output of systems based on the quantized data. The second is to identify the unknown parameter based on the input and the estimation of the noise-free output. The identification algorithm is also summarized. Asymptotic convergence of the estimators is analyzed in detail, which shows that the estimators are convergent almost everywhere. A numerical example is given to illustrate the results obtained in this paper.

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Identification of Linear Models From Quantized Data: A Midpoint-Projection Approach

Cited by 15 publications

References 35 publications

Network Weight Estimation for Binary-Valued Observation Models

Network Weight Estimation for Binary-Valued Observation Models

Granger Causality of Gaussian Signals from Quantized Measurements

Identification of Rational Systems with Logarithmic Quantized Data

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