| 1. |
- Abdalmoaty, Mohamed, 1986-, et al.
(author)
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Continuous Time-Delay Estimation From Sampled Measurements
- 2023
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In: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 56:2, s. 6982-6987
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Journal article (peer-reviewed)abstract
- An algorithm for continuous time-delay estimation from sampled output data and a known input of finite energy is presented. The continuous time-delay modeling allows for the estimation of subsample delays. The proposed estimation algorithm consists of two steps. First, the continuous Laguerre spectrum of the output (delayed) signal is estimated from discretetime (sampled) noisy measurements. Second, an estimate of the delay value is obtained via a Laguerre domain model using a continuous-time description of the input. The second step of the algorithm is shown to be intrinsically biased, the bias sources are established, and the bias itself is modeled. The proposed delay estimation approach is compared in a Monte-Carlo simulation with state-of-the-art methods implemented in time, frequency, and Laguerre domain demonstrating comparable or higher accuracy in the considered scenario.
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| 2. |
- Abdalmoaty, Mohamed, 1986-, et al.
(author)
-
Identification of Stochastic Nonlinear Models Using Optimal Estimating Functions
- 2020
-
In: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 119
-
Journal article (peer-reviewed)abstract
- The first part of the paper examines the asymptotic properties of linear prediction error method estimators, which were recently suggested for the identification of nonlinear stochastic dynamical models. It is shown that their accuracy depends not only on the shape of the unknown distribution of the data, but also on how the model is parameterized. Therefore, it is not obvious in general which linear prediction error method should be preferred. In the second part, the estimating functions approach is introduced and used to construct estimators that are asymptotically optimal with respect to a specific class of estimators. These estimators rely on a partial probabilistic parametric models, and therefore neither require the computations of the likelihood function nor any marginalization integrals. The convergence and consistency of the proposed estimators are established under standard regularity and identifiability assumptions akin to those of prediction error methods. The paper is concluded by several numerical simulation examples.
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| 3. |
- Abdalmoaty, Mohamed, 1986-, et al.
(author)
-
Noise reduction in Laguerre-domain discrete delay estimation
- 2022
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In: 2022 IEEE 61st Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665467612 - 9781665467605 - 9781665467629 ; , s. 6254-6259
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Conference paper (peer-reviewed)abstract
- This paper introduces a stochastic framework for a recently proposed discrete-time delay estimation method in Laguerre-domain, i.e. with the delay block input and output signals being represented by the corresponding Laguerre series. A novel Laguerre-domain disturbance model allowing the involved signals to be square-summable sequences is devised. The relation to two commonly used time-domain disturbance models is clarified. Furthermore, by forming the input signal in a certain way, the signal shape of an additive output disturbance can be estimated and utilized for noise reduction. It is demonstrated that a significant improvement in the delay estimation error is achieved when the noise sequence is correlated. The noise reduction approach is applicable to other Laguerre-domain problems than pure delay estimation.
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| 4. |
- Abdalmoaty, Mohamed, 1986-, et al.
(author)
-
Privacy and Security in Network Controlled Systems via Dynamic Masking
- 2023
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In: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 56:2, s. 991-996
-
Journal article (peer-reviewed)abstract
- In this paper, we propose a new architecture to enhance the privacy and security of networked control systems against malicious adversaries. We consider an adversary which first learns the system using system identification techniques (privacy), and then performs a data injection attack (security). In particular, we consider an adversary conducting zero-dynamics attacks (ZDA) which maximizes the performance cost of the system whilst staying undetected. Using the proposed architecture, we show that it is possible to (i) introduce significant bias in the system estimates obtained by the adversary: thus providing privacy, and (ii) efficiently detect attacks when the adversary performs a ZDA using the identified system: thus providing security. Through numerical simulations, we illustrate the efficacy of the proposed architecture
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| 5. |
- Abdalmoaty, Mohamed R. H., 1986-, et al.
(author)
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Identification of Non-Linear Differential-Algebraic Equation Models with Process Disturbances
- 2021
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In: 2021 60th IEEE Conference on Decision and Control (CDC). - : IEEE. - 9781665436595 - 9781665436588 - 9781665436601 ; , s. 2300-2305
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Conference paper (peer-reviewed)abstract
- Differential-algebraic equations (DAEs) arise naturally as a result of equation-based object-oriented modeling. In many cases, these models contain unknown parameters that have to be estimated using experimental data. However, often the system is subject to unknown disturbances which, if not taken into account in the estimation, can severely affect the model's accuracy. For non-linear state-space models, particle filter methods have been developed to tackle this issue. Unfortunately, applying such methods to non-linear DAEs requires a transformation into a state-space form, which is particularly difficult to obtain for models with process disturbances. In this paper, we propose a simulation-based prediction error method that can be used for non-linear DAEs where disturbances are modeled as continuous-time stochastic processes. To the authors' best knowledge, there are no general methods successfully dealing with parameter estimation for this type of model. One of the challenges in particle filtering methods are random variations in the minimized cost function due to the nature of the algorithm. In our approach, a similar phenomenon occurs and we explicitly consider how to sample the underlying continuous process to mitigate this problem. The method is illustrated numerically on a pendulum example. The results suggest that the method is able to deliver consistent estimates.
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| 6. |
- Abdalmoaty, Mohamed, 1986-, et al.
(author)
-
The Gaussian MLE versus the Optimally weighted LSE
- 2020
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In: IEEE signal processing magazine (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-5888 .- 1558-0792. ; 37:6, s. 195-199
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Journal article (peer-reviewed)abstract
- In this note, we derive and compare the asymptotic covariance matrices of two parametric estimators: the Gaussian Maximum Likelihood Estimator (MLE), and the optimally weighted Least-Squares Estimator (LSE). We assume a general model parameterization where the model's mean and variance are jointly parameterized, and consider Gaussian and non-Gaussian data distributions.
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| 7. |
- Anubhab, Ghosh, et al.
(author)
-
Time-Varying Normalizing Flows for Dynamical Signals
- 2022
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Conference paper (peer-reviewed)abstract
- We develop a time-varying normalizing flow (TVNF) for explicit generative modeling of dynamical signals. Being explicit, it can generate samples of dynamical signals, and compute the likelihood of a (given) dynamical signal sample. In the proposed model, signal flow in the layers of the normalizing flow is a function of time, which is realized using an encoded representation that is the output of a recurrent neural network (RNN). Given a set of dynamical signals, the parameters of TVNF are learned according to a maximum-likelihood approach in conjunction with gradient descent (backpropagation). Use of the proposed model is illustrated for a toy application scenario-maximum-likelihood based speech-phone classification task.
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| 8. |
- Bereza-Jarocinski, Robert, et al.
(author)
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Stochastic Approximation for Identification of Non-Linear Differential-Algebraic Equations with Process Disturbances
- 2022
-
In: 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665467612 - 9781665467605 - 9781665467629 ; , s. 6712-6717
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Conference paper (peer-reviewed)abstract
- Differential-algebraic equations, commonly used to model physical systems, are the basis for many equation-based object-oriented modeling languages. When systems described by such equations are influenced by unknown process disturbances, estimating unknown parameters from experimental data becomes difficult. This is because of problems with the existence of well-defined solutions and the computational tractability of estimators. In this paper, we propose a way to minimize a cost function-whose minimizer is a consistent estimator of the true parameters-using stochastic gradient descent. This approach scales significantly better with the number of unknown parameters than other currently available methods for the same type of problem. The performance of the method is demonstrated through a simulation study with three unknown parameters. The experiments show a significantly reduced variance of the estimator, compared to an output error method neglecting the influence of process disturbances, as well as an ability to reduce the estimation bias of parameters that the output error method particularly struggles with.
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| 9. |
- Ghosh, Anubhab, et al.
(author)
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DeepBayes—An estimator for parameter estimation in stochastic nonlinear dynamical models
- 2024
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In: Automatica. - : Elsevier Ltd. - 0005-1098 .- 1873-2836. ; 159
-
Journal article (peer-reviewed)abstract
- Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The majority of existing methods employ maximum likelihood or Bayesian estimation. However, these methods suffer from some limitations, most notably the substantial computational time for inference coupled with limited flexibility in application. In this work, we propose DeepBayes estimators that leverage the power of deep recurrent neural networks. The method consists of first training a recurrent neural network to minimize the mean-squared estimation error over a set of synthetically generated data using models drawn from the model set of interest. The a priori trained estimator can then be used directly for inference by evaluating the network with the estimation data. The deep recurrent neural network architectures can be trained offline and ensure significant time savings during inference. We experiment with two popular recurrent neural networks — long short term memory network (LSTM) and gated recurrent unit (GRU). We demonstrate the applicability of our proposed method on different example models and perform detailed comparisons with state-of-the-art approaches. We also provide a study on a real-world nonlinear benchmark problem. The experimental evaluations show that the proposed approach is asymptotically as good as the Bayes estimator.
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| 10. |
- Ghosh, Anubhab, et al.
(author)
-
Time-varying Normalizing Flow for Generative Modeling of Dynamical Signals
- 2022
-
In: 2022 30Th European Signal Processing Conference (EUSIPCO 2022). - : IEEE. - 9789082797091 - 9781665467995 ; , s. 1492-1496
-
Conference paper (peer-reviewed)abstract
- We develop a time-varying normalizing flow (TVNF) for explicit generative modeling of dynamical signals. Being explicit, it can generate samples of dynamical signals, and compute the likelihood of a (given) dynamical signal sample. In the proposed model, signal flow in the layers of the normalizing flow is a function of time, which is realized using an encoded representation that is the output of a recurrent neural network (RNN). Given a set of dynamical signals, the parameters of TVNF are learned according to maximum-likelihood approach in conjunction with gradient descent (backpropagation). Use of the proposed model is illustrated for a toy application scenario - maximum-likelihood based speech-phone classification task.
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