1. 
 Abdalmoaty, Mohamed, et al.
(författare)

On ReWeighting, Regularization Selection, and Transient in Nuclear Norm Based Identification
 2015

Konferensbidrag (refereegranskat)abstract
 In this contribution, we consider the classical problem of estimating an Output Error model given a set of inputoutput measurements. First, we develop a regularization method based on the reweighted nuclear norm heuristic. We show that the reweighting improves the estimate in terms of better fit. Second, we suggest an implementation method that helps in eliminating the regularization parameters from the problem by introducing a constant based on a validation criterion. Finally, we develop a method for considering the effect of the transient when the initial conditions are unknown. A simple numerical example is used to demonstrate the proposed method in comparison to classical and another recent method based on the nuclear norm heuristic.


2. 
 Abdalmoaty, Mohamed R., 1986, et al.
(författare)

Application of a Linear PEM Estimator to a Stochastic WienerHammerstein Benchmark Problem
 2018

Ingår i: 18th IFAC Symposium on System Identification.

Konferensbidrag (refereegranskat)abstract
 The estimation problem of stochastic WienerHammerstein models is recognized to be challenging, mainly due to the analytical intractability of the likelihood function. In this contribution, we apply a computationally attractive prediction error method estimator to a realdata stochastic WienerHammerstein benchmark problem. The estimator is defined using a deterministic predictor that is nonlinear in the input. The prediction error method results in tractable expressions, and Monte Carlo approximations are not necessary. This allows us to tackle several issues considered challenging from the perspective of the current mainstream approach. Under mild conditions, the estimator can be shown to be consistent and asymptotically normal. The results of the method applied to the benchmark data are presentedand discussed.


3. 


4. 
 Abdalmoaty, Mohamed R., 1986, et al.
(författare)

Identication of a Class of Nonlinear Dynamical Networks
 2018

Konferensbidrag (refereegranskat)abstract
 Identifcation of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear timeinvariant networks. Meanwhile, most reallife systems exhibit nonlinear behaviors; consider, for example, two stochastic linear timeinvariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal onestep ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closedform analytical expressions in several nontrivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some blockoriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.


5. 
 Abdalmoaty, Mohamed R., 1986, et al.
(författare)

Linear Prediction Error Methods for Stochastic Nonlinear Models
 2018

Ingår i: Automatica.  Elsevier.  00051098.

Tidskriftsartikel (refereegranskat)abstract
 The estimation problem for stochastic parametric nonlinear dynamical models is recognized to be challenging. The main difficulty is the intractability of the likelihood function and the optimal onestep ahead predictor. In this paper, we present relatively simple prediction error methods based on nonstationary predictors that are linear in the outputs. They can be seen as extensions of the linear identification methods for the case where the hypothesized model is stochastic and nonlinear. The resulting estimators are defined by analytically tractable objective functions in several common cases. It is shown that, under certain identifiability and standard regularity conditions, the estimators are consistent and asymptotically normal. We discuss the relationship between the suggested estimators and those based on secondorder equivalent models as well as the maximum likelihood method. The paper is concluded with a numerical simulation example as well as a realdata benchmark problem.


6. 
 Abdalmoaty, Mohamed, 1986, et al.
(författare)

Simulated Pseudo Maximum Likelihood Identification of Nonlinear Models
 2017

Ingår i: The 20th IFAC World Congress.  Elsevier. ; s. 1405814063

Konferensbidrag (refereegranskat)abstract
 Nonlinear stochastic parametric models are widely used in various fields. However, for these models, the problem of maximum likelihood identification is very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the analytically intractable likelihood function and compute either the maximum likelihood or a Bayesian estimator. These methods, albeit asymptotically optimal, are computationally expensive. In this contribution, we present a simulationbased pseudo likelihood estimator for nonlinear stochastic models. It relies only on the first two moments of the model, which are easy to approximate using MonteCarlo simulations on the model. The resulting estimator is consistent and asymptotically normal. We show that the pseudo maximum likelihood estimator, based on a multivariate normal family, solves a prediction error minimization problem using a parameterized norm and an implicit linear predictor. In the light of this interpretation, we compare with the predictor defined by an ensemble Kalman filter. Although not identical, simulations indicate a close relationship. The performance of the simulated pseudo maximum likelihood method is illustrated in three examples. They include a challenging statespace model of dimension 100 with one output and 2 unknown parameters, as well as an applicationmotivated model with 5 states, 2 outputs and 5 unknown parameters.


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8. 
 Bombois, X., et al.
(författare)

Optimal identification experiment design for the interconnection of locally controlled systems
 2018

Ingår i: Automatica.  Elsevier.  00051098. ; 89, s. 169179

Tidskriftsartikel (refereegranskat)abstract
 This paper considers the identification of the modules of a network of locally controlled systems (multiagent systems). Its main contribution is to determine the least perturbing identification experiment that will nevertheless lead to sufficiently accurate models of each module for the global performance of the network to be improved by a redesign of the decentralized controllers. Another contribution is to determine the experimental conditions under which sufficiently informative data (i.e. data leading to a consistent estimate) can be collected for the identification of any module in such a network.


9. 
 Bottegal, Giulio, et al.
(författare)

A new kernelbased approach to system identification with quantized output data
 2017

Ingår i: Automatica.  00051098. ; 85, s. 145152

Tidskriftsartikel (refereegranskat)abstract
 In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zeromean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. This serves as a starting point to cast our system identification problem into a Bayesian framework. We employ Markov Chain Monte Carlo methods to provide an estimate of the system. In particular, we design two methods based on the socalled Gibbs sampler that allow also to estimate the kernel hyperparameters by marginal likelihood maximization via the expectationmaximization method. Numerical simulations show the effectiveness of the proposed scheme, as compared to the stateoftheart kernelbased methods when these are employed in system identification with quantized data. (C) 2017 Elsevier Ltd. All rights reserved.


10. 
 Bottegal, Giulio, et al.
(författare)

Bayesian kernelbased system identification with quantized output data
 2015

Ingår i: IFACPapersOnLine.  Elsevier.  24058963. ; 48:28, s. 455460

Tidskriftsartikel (refereegranskat)abstract
 In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zeromean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. This serves as a starting point to cast our system identification problem into a Bayesian framework. We employ Markov Chain Monte Carlo (MCMC) methods to provide an estimate of the system. In particular, we show how to design a Gibbs sampler which quickly converges to the target distribution. Numerical simulations show a substantial improvement in the accuracy of the estimates over stateoftheart kernelbased methods when employed in identification of systems with quantized data.

