25 results on '"U. Soverini"'
Search Results
2. A Modular Approach in Designing an Environment for Teaching System Identification
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Paolo Castaldi, Roberto Diversi, U. Soverini, and Roberto Guidorzi
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Identification (information) ,Class (computer programming) ,Point (typography) ,business.industry ,Computer science ,System identification ,Systems engineering ,The Internet ,Modular design ,business ,Software engineering - Abstract
Identification procedures, while based on abstract (mathematical) concepts and procedures, must be applied to processes belonging to the real world. This faces teachers with the challenge of transmitting to their students adequate theories and of guiding their practice in identifying processes not belonging to any considered class of models. This requires, from an educational point of view, the use of suitable environments endowed with a proper mix of theoretical tools and of practical application opportunities. This paper describes an experience in delivering an identification course through the Internet and the results that have been obtained so far.
- Published
- 2000
3. Rank Reducibility of a Covariance Matrix in the Frisch Scheme
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Paolo Castaldi, S. Beghelli, and U. Soverini
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Set (abstract data type) ,Polynomial ,Mathematical optimization ,Mathematical model ,Rank (linear algebra) ,Covariance matrix ,Diagonal ,Errors-in-variables models ,Applied mathematics ,Point (geometry) ,Mathematics - Abstract
The Frisch scheme for identification of mathematical models from data corrupted by additive noise contains many unsolved aspects. One of the principal problems, of particular interest for factor analysis and structural regression methodologies, concerns rank reducibility of a covariance matrix simply by changing its diagonal entries. With reference to this topic, the paper shows that the mathematical models compatible with the data are the solutions of a set of polynomial equations which satisfy some well-defined constraints. The approach is based on the rank reducibility criteria suggested in a well-known paper by Ledermann, generalized to take into account the definiteness conditions on the noise-free covariance matrix. The results obtained give a deeper insight on the theoretical properties of the Frisch scheme and can represent a starting point for the design of numerical algorithms to solve the problem.
- Published
- 1996
4. Identification of dynamic errors-in-variables models
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Paolo Castaldi and U. Soverini
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Discrete system ,Control and Systems Engineering ,Linear system ,Scalar (mathematics) ,System identification ,Calculus ,Applied mathematics ,Identifiability ,Errors-in-variables models ,Spectral theorem ,Electrical and Electronic Engineering ,Time series ,Mathematics - Abstract
This paper deals with the identifiability of scalar dynamic errors-in-variables models characterized by rational spectra. The hypothesis of causality for the underlying dynamic system is taken into account. By making use of stochastic realization theory and of the structural properties of state-space representations, it is shown that, under mild assumptions, the model is uniquely identified.
- Published
- 1996
5. The Frisch Identification Scheme: Properties of the Solution in the Dynamic Case
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Paolo Castaldi, S. Beghelli, Roberto Guidorzi, and U. Soverini
- Subjects
Mathematical optimization ,Identification scheme ,Estimation theory ,Computer science ,Scheme (mathematics) ,Linear system ,System identification ,Selection criterion - Abstract
This paper investigates some of the many algebraic properties of the solution of the Frisch identification scheme applied to dynamic systems. These properties are related to the design of a robust selection criterion leading to a single model also when the assumptions of the scheme are not fulfilled.
- Published
- 1994
6. Congruence Conditions Between System Identification and Kalman Filtering
- Author
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S. Beghelli, U. Soverini, Roberto Guidorzi, and Paolo Castaldi
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Extended Kalman filter ,Control theory ,Filtering problem ,Fast Kalman filter ,Observability ,Linear-quadratic-Gaussian control ,Alpha beta filter ,Kalman decomposition ,Invariant extended Kalman filter ,Mathematics - Abstract
In this paper consideration is given to the problem of determining an optimal estimate of the output of a linear dynamic SISO system from the knowledge of the input-output data corrupted by additive noise. The solution of this problem can be divided into two steps: first. the model of the system and of the noise affecting the data is identified. then a Kalman filter is designed on the basis of this model. Since the result of the identification scheme may be a whole family of models. a comparison between these different systems is analyzed with refcrence to the behavior of the associated Kalman filters. Some silmulation results are finally discussed.
- Published
- 1992
7. A comparison among different inversion methods for multi-exponential NMR relaxation data
- Author
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Paola Fantazzini, Paolo Castaldi, Villiam Bortolotti, U. Soverini, Robert J. S. Brown, and G.C. Borgia
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Magnetic Resonance Spectroscopy ,Inversion methods ,Biomedical Engineering ,Biophysics ,Inversion (meteorology) ,Exponential function ,Maxima and minima ,Continuous distributions ,Humans ,Radiology, Nuclear Medicine and imaging ,Maxima ,Algorithm ,Porosity ,Algorithms ,Mathematics - Abstract
The inversion of data to be represented by sums or continuous distributions of exponentials is done by different algorithms and compared. The published CONTIN program presents a chosen solution with an appropriate amount of detail. An in-house program EXDISTR allows operative choice of various constraints in order to show the consequences in quality of fit of allowing various features such as extra maxima or minima. Another in-house program based on the system theory approach, IDENT, treats the data as the output samples of a linear, time-invariant, autonomous dynamic system.
- Published
- 1994
8. Identification of multivariable errors-in-variables models
- Author
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Paolo Castaldi, Roberto Diversi, U. Soverini, and Roberto Guidorzi
- Subjects
Noise ,Identification (information) ,Noise measurement ,Congruence (geometry) ,Control theory ,Multivariable calculus ,MIMO ,Errors-in-variables models ,Covariance ,Algorithm ,Mathematics - Abstract
The paper deals with a new identification approach, based on a prediction error method, for multivariable errors-in-variables models (EIV). Starting from the ARMAX decomposition of MIMO EIV processes and congruence conditions between noisy sequences and the constraints of EIV representations, the simultaneous estimate of the model parameters and of the noise covariance matrices is obtained. Numerical simulations are included to illustrate the effectiveness of the proposed algorithm.
9. A frequential approach for errors-in-variables models
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S. Beghelli, Paolo Castaldi, and U. Soverini
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Identification (information) ,Control theory ,Linear system ,Process (computing) ,Errors-in-variables models ,Algorithm ,Transfer function ,Mathematics - Abstract
This paper presents an identification method for errors-in-variables systems with input-output measurements affected by white and mutually correlated noises. The procedure, based on a frequency-domain approach, allows to uniquely determine both the characteristics of the noises affecting the data and the transfer function of the process under investigation. A numerical example is reported in order to illustrate the suggested technique and to verify its numerical implementation.
10. Robust residual generation for dynamic processes using de-coupling technique
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Silvio Simani, U. Soverini, and Roberto Diversi
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Reliability theory ,Engineering ,Signal generator ,business.industry ,fault diagnosis ,gas turbines ,process monitoring ,reliability theory ,signal detection ,Control engineering ,Residual ,Fault detection and isolation ,Control theory ,Robustness (computer science) ,Detection theory ,Actuator ,business ,Decoupling (electronics) - Abstract
The work presents some results concerning robust fault detection for dynamic processes using a disturbance decoupling technique. The first step of the approach consists of exploiting input-output descriptions of the monitored system. In particular, the disturbance term of a model can be used to take into account unknown inputs affecting the system. The next step of the scheme leads to definition of a set of parity relations that can be used as robust residual signals since they are insensitive to the disturbance term. The proposed fault detection procedure has been tested on an industrial process simulator. Sensor and actuator faults have been simulated on a gas turbine model.
11. Identification of errors–in–variables models with mutually correlated input and output noises
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Umberto Soverini, Roberto Guidorzi, Roberto Diversi, R. Diversi, R. Guidorzi, and U. Soverini
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IDENTIFICATION ,Monte Carlo method ,Extension (predicate logic) ,Parameter identification problem ,Set (abstract data type) ,ERRORS–IN–VARIABLES MODELS ,Identification (information) ,Noise ,Control theory ,MUTUALLY CORRELATED NOISES ,FRISCH SCHEME ,Errors-in-variables models ,Locus (mathematics) ,Algorithm ,Mathematics - Abstract
This paper deals with the identification of errors–in–variables models where the additive input and output noises are mutually correlated white processes. The proposed solution is based on the extension of the dynamic Frisch scheme introduced in (Beghelli et al., 1990). First, a geometric characterization of the whole set of admissible solutions in the noise space is described. Then, a criterion that allows to select the solution of the identification problem inside the locus is proposed. This criterion relies on the properties of a set of high–order Yule–Walker equations. The effectiveness of this identification approach is tested by means of Monte Carlo simulations.
- Published
- 2012
12. Identification of ARMAX models with noisy input and output
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Umberto Soverini, Roberto Guidorzi, Roberto Diversi, S. BITTANTI, A. CENEDESE, S. ZAMPIERI, R. Diversi, R. Guidorzi, and U. Soverini
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Engineering ,SYSTEM IDENTIFICATION ,Observational error ,business.industry ,Monte Carlo method ,System identification ,Linear model ,Process (computing) ,LINEAR MODELS ,ERRORS–IN–VARIABLES MODELS ,Identification (information) ,Noise ,ARMAX MODELS ,Control theory ,Errors-in-variables models ,business - Abstract
ARMAX models are widely used in identification and are a standard tool in control engineering for both system description and control design. These models, however, can be non realistic in many practical contexts because of the presence of measurement errors that play an important role in applications like fault diagnosis and optimal filtering. ARMAX models can be enhanced by introducing also additive error terms on the input and output observations. This scheme, that can be denoted as “ARMAX + noise”, belongs to the errors–in–variables family and allows taking into account the presence of both process disturbances and measurement noise. This paper proposes a three-step procedure for identifying “ARMAX + noise” processes. The first step of the identification algorithm in based on an iterative search procedure while the second and third ones rely on simple least–squares formulas. The paper reports also the results of some Monte Carlo simulations that underline the effectiveness of the proposed approach.
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- 2011
13. Identification of ARMAX models with additive output noise
- Author
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Umberto Soverini, Roberto Diversi, Roberto Guidorzi, R. Diversi, R. Guidorzi, and U. Soverini
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ERRORS–IN–VARIABLES ,Engineering ,SYSTEM IDENTIFICATION ,business.industry ,Instrumental variable ,Monte Carlo method ,Linear model ,System identification ,White noise ,LINEAR MODELS ,Noise ,Identification (information) ,ARMAX MODELS ,Control theory ,Errors-in-variables models ,INSTRUMENTAL VARIABLE ,business - Abstract
ARMAX models constitute an excellent compromise between performance and complexity and can model in an effective way the presence of disturbances acting on the process state. These models, however, do not take into account the observation errors on the output of the process to be identified and this can be particularly important in applications like filtering and fault diagnosis. This paper concerns extended ARMAX models that consider also the presence of additive white noise on the output observation and describes an approach for their identification that takes advantage of both the errors–in–variables framework and the instrumental variable properties. The paper reports also the results of Monte Carlo simulations that underline the effectiveness of the proposed approach.
- Published
- 2009
14. Identification of ARARX models in presence of additive noise
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Umberto Soverini, Roberto Guidorzi, Roberto Diversi, M. J. CHUNG, P. MISRA, H. SHIM, R. Diversi, R. Guidorzi, and U. Soverini
- Subjects
Engineering ,SYSTEM IDENTIFICATION ,Observational error ,business.industry ,Monte Carlo method ,Linear system ,Process (computing) ,System identification ,LINEAR SYSTEMS ,ARARX MODELS ,ERRORS–IN–VARIABLES MODELS ,Noise ,Identification (information) ,Errors-in-variables models ,business ,Algorithm ,Simulation - Abstract
The identification of dynamic processes can be performed by means of different classes of models relying on different stochastic environments to describe the misfit between the model and process observations. This paper introduces a new class of models by considering additive error terms on the observations of the input and output of ARARX models and proposes a three–step identification procedure for their identification. ARARX + noise models extend the traditional ARARX or ARMAX ones and can be seen as errors–in–variables models where both measurement errors and process disturbances are taken into account. The results of Monte Carlo simulations show the good performance of the proposed identification procedure.
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- 2008
15. Identification of autoregressive models in the presence of additive noise
- Author
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Roberto Guidorzi, Umberto Soverini, Roberto Diversi, R. Diversi, R. Guidorzi, and U. Soverini
- Subjects
SYSTEM IDENTIFICATION ,Speech recognition ,Monte Carlo method ,YULE–WALKER EQUATIONS ,System identification ,White noise ,Noise ,Autoregressive model ,Positive definiteness ,Control and Systems Engineering ,Autocorrelation matrix ,Signal Processing ,NOISY AUTOREGRESSIVE MODELS ,Electrical and Electronic Engineering ,Algorithm ,STAR model ,Mathematics - Abstract
A common approach in modeling signals in many engineering applications consists in adopting autoregressive (AR) models, consisting in filters with transfer functions having a unitary numerator, driven by white noise. Despite their wide application, these models do not take into account the possible presence of errors on the observations and cannot prove accurate when these errors are significant. AR plus noise models constitute an extension of AR models that consider also the presence of an observation noise. This paper describes a new algorithm for the identification of AR plus noise models that is characterized by a very good compromise between accuracy and efficiency. This algorithm, taking advantage of both low and high-order Yule–Walker equations, also guarantees the positive definiteness of the autocorrelation matrix of the estimated process and allows to estimate the equation error and observation noise variances. It is also shown how the proposed procedure can be used for estimating the order of the AR model. The new algorithm is compared with some traditional algorithms by means of Monte Carlo simulations. Copyright © 2007 John Wiley & Sons, Ltd.
- Published
- 2008
16. Comparison of three Frisch methods for errors-in-variables identification
- Author
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Torsten Söderström, Mei Hong, Umberto Soverini, Roberto Diversi, M. J. CHUNG, P. MISRA, H. SHIM, M. Hong, T. Soderstrom, U. Soverini, and R. Diversi
- Subjects
Estimation ,SYSTEM IDENTIFICATION ,Basis (linear algebra) ,Computer science ,Linear system ,System identification ,Context (language use) ,General Medicine ,Dynamical system ,LINEAR SYSTEMS ,DYNAMIC FRISCH SCHEME ,ERRORS–IN–VARIABLES MODELS ,Matrix (mathematics) ,Identification (information) ,Noise ,Control theory ,Errors-in-variables models ,Algorithm - Abstract
The errors–in–variables framework concerns static or dynamic systems whose input and output variables are affected by additive noise. Several estimation methods have been proposed for identifying dynamic errors–in–variables models. One of the more promising approaches is the so–called Frisch scheme. This paper decribes three different estimation criteria within the Frisch context and compares their estimation accuracy on the basis of the asymptotic covariance matrices of the estimates. Some numerical examples support well the theoretical results.
- Published
- 2008
17. Speech Enhancement Combining Optimal Smoothing and Errors-In-Variables Identification of Noisy AR Processes
- Author
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William Bobillet, Umberto Soverini, Roberto Guidorzi, E. Grivel, Roberto Diversi, Mohamed Najim, Grivel, Eric, W. Bobillet, R. Diversi, E. Grivel, R. Guidorzi, M. Najim, and U. Soverini
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SYSTEM IDENTIFICATION ,[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing ,Estimation theory ,Speech recognition ,OPTIMAL SMOOTHING ,KALMAN FILTERING ,Kalman filter ,Speech processing ,Speech enhancement ,SPEECH ENHANCEMENT ,Noise ,Signal-to-noise ratio ,Autoregressive model ,Signal Processing ,AUTOREGRESSIVE MODELS ,Electrical and Electronic Engineering ,ComputingMilieux_MISCELLANEOUS ,Smoothing ,[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing ,Mathematics - Abstract
In the framework of speech enhancement, several parametric approaches based on an a priori model for a speech signal have been proposed. When using an autoregressive (AR) model, three issues must be addressed. (1) How to deal with AR parameter estimation? Indeed, due to additive noise, the standard least squares criterion leads to biased estimates of AR parameters. (2) Can an estimation of the variance of the additive noise for each speech frame be obtained? A voice activity detector is often used for its estimation. (3) Which estimation rules and techniques (filtering, smoothing, etc.) can be considered to retrieve the speech signal? Our contribution in this paper is threefold. First, we propose to view the identification of the noisy AR process as an errors-in-variables problem. This blind method has the advantage of providing accurate estimations of both the AR parameters and the variance of the additive noise. Second, we propose an alternative algorithm to standard Kalman smoothing, based on a constrained minimum variance estimation procedure with a lower computational cost. Third, the combination of these two steps is investigated. It provides better results than some existing speech enhancement approaches in terms of signal-to-noise-ratio (SNR), segmental SNR, and informal subjective tests.
- Published
- 2007
18. Maximum likelihood identification of noisy input–output models
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Roberto Diversi, Roberto Guidorzi, Umberto Soverini, R. Diversi, R. Guidorzi, and U. Soverini
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Input/output ,SYSTEM IDENTIFICATION ,Iterative method ,Monte Carlo method ,System identification ,INTERPOLATION ,CRAMER-RAO LOWER BOUND ,White noise ,Parameter identification problem ,symbols.namesake ,Control and Systems Engineering ,Gaussian noise ,ERRORS-IN-VARIABLES MODELS ,Statistics ,MAXIMUM LIKELIHOOD IDENTIFICATION ,symbols ,Errors-in-variables models ,Electrical and Electronic Engineering ,Algorithm ,Mathematics - Abstract
This work deals with the identification of errors-in-variables models corrupted by white and uncorrelated Gaussian noises. By introducing an auxiliary process, it is possible to obtain a maximum likelihood solution of this identification problem, by means of a two-step iterative algorithm. This approach allows also to estimate, as a byproduct, the noise-free input and output sequences. Moreover, an analytic expression of the finite Cramer-Rao lower bound is derived. The method does not require any particular assumption on the input process, however, the ratio of the noise variances is assumed as known. The effectiveness of the proposed algorithm has been verified by means of Monte Carlo simulations.
- Published
- 2007
19. Kalman filtering in extended noise environments
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Roberto Diversi, Umberto Soverini, Roberto Guidorzi, R. Diversi, R. Guidorzi, and U. Soverini
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Optimal estimation ,Monte Carlo method ,KALMAN FILTERING ,Kalman filter ,Variance (accounting) ,Computer Science Applications ,ERRORS-IN-VARIABLES FILTERING ,Noise ,Control and Systems Engineering ,Control theory ,Input estimation ,RECURSIVE FILTERING ,OPTIMAL FILTERING ,Fast Kalman filter ,State (computer science) ,Electrical and Electronic Engineering ,Mathematics - Abstract
This paper introduces an extended environment for Kalman filtering that considers also the presence of additive noise on input observations in order to solve the problem of optimal (minimal variance) estimation of noise-corrupted input and output sequences. This environment includes as subcases both errors-in-variables filtering (optimal estimate of inputs and outputs from noisy observations) and traditional Kalman filtering (optimal stimate of state and output in presence of state and output noise). A Monte Carlo simulation shows that the performance of this extended filtering technique leads to the expected minimal variance estimates.
- Published
- 2005
20. A NEW ESTIMATION APPROACH FOR AR MODELS IN PRESENCE OF NOISE
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Umberto Soverini, Roberto Guidorzi, Roberto Diversi, P. HORACEK, M. SIMANDL, P. ZITEK, R. Diversi, U. Soverini, and R. Guidorzi
- Subjects
PARAMETER ESTIMATION ,Noise ,SYSTEM IDENTIFICATION ,Autoregressive model ,Autocorrelation matrix ,Estimation theory ,Computer science ,Statistics ,Monte Carlo method ,System identification ,AUTOREGRESSIVE MODELS ,White noise ,Algorithm - Abstract
This paper considers the problem of estimating the parameters of an autoregressive (AR) process in presence of additive white noise and proposes a new identification method, based on theoretical results originally developed in errors-in-variables contexts. This approach allows to estimate the AR parameters, the driving noise variance and the variance of the additive noise in a congruent way in that these estimates assure the positive definiteness of the autocorrelation matrix. The performance of the proposed algorithm is compared with that of bias-compensated least-squares methods by means fo Monte Carlo simulations. The results show the effectivenesss of the new method also in presence of high amounts of noise.
- Published
- 2005
21. Blind identification and equalization of two-channel FIR systems in unbalanced noise environments
- Author
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Roberto Diversi, Umberto Soverini, Roberto Guidorzi, R. Diversi, R. Guidorzi, and U. Soverini
- Subjects
Engineering ,business.industry ,Monte Carlo method ,System identification ,Equalization (audio) ,LINEAR SYSTEMS ,Background noise ,BLIND IDENTIFICATION ,Noise ,Identification (information) ,FIR SYSTEMS ,Control and Systems Engineering ,BLIND EQUALIZATION ,Signal Processing ,Electronic engineering ,Computer Vision and Pattern Recognition ,Deconvolution ,Electrical and Electronic Engineering ,business ,Algorithm ,Software ,Blind equalization - Abstract
Blind identification is a very significant problem in many contexts where only the output of transmission channels can be observed. The solutions that can be found in the literature are limited to the case of equal amounts of additive noise on the observations; this paper proposes new identification procedures that can be applied to the case of two FIR channels affected by unknown and unbalanced amounts of additive noise. The identified models are then used for the minimal variance deconvolution of the unknown input signal. Several Monte Carlo simulations confirm the good performance of these procedures also in severe SNR conditions.
- Published
- 2005
22. Direction–of–Arrival Estimation in Nonuniform Noise Fields: A Frisch Scheme Approach
- Author
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Roberto Guidorzi, Umberto Soverini, Roberto Diversi, J. Swiątek, A. Grzech, P. Swiątek, J. M. Tomczak, R. Diversi, R. Guidorzi, and U. Soverini
- Subjects
Scheme (programming language) ,Mathematical optimization ,Covariance matrix ,Monte Carlo method ,Direction of arrival ,Direction of arrival estimation ,White noise ,Identification (information) ,Noise ,FRISCH SCHEME ,nonuniform noise ,computer ,Algorithm ,Mathematics ,computer.programming_language - Abstract
This paper proposes a two-step identification procedure for the direction-of-arrival estimation problem in the presence of nonuniform white noise. The first step consists in estimating the unknown sensor noise variances by exploiting the properties of the Frisch scheme. Once that the noise covariance matrix has been identified, the angles of arrival are computed by using the classical ESPRIT algorithm. The effectiveness of the whole procedure is tested by means of Monte Carlo simulations.
- Published
- 2014
23. Identification of residual generators for fault detection of linear dynamic models
- Author
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Umberto Soverini, Roberto Diversi, Silvio Simani, S. Simani, R. Diversi, and U. Soverini
- Subjects
Polynomial ,Residuals ,SYSTEM IDENTIFICATION ,Basis (linear algebra) ,CANONICAL INPUT-OUTPUT POLYNOMIAL FORMS ,RESIDUAL GENERATORS ,Multivariable calculus ,Monte Carlo method ,System identification ,Ambientale ,FAULT DETECTION ,fault diagnosis ,Residual ,Function Design ,Fault detection and isolation ,Control theory ,Linear Multivariable Systems ,dynamic process ,Subspace topology ,Mathematics - Abstract
Classical model-based fault detection schemes for linear multivariable systems require the definition of suitable residual functions. This paper shows the possibility of identifying residual generators even when the system model is unknown, by following a black-box approach. The result is obtained by using canonical input-output polynomial forms which lead to characterise in a straightforward fashion the basis of the subspace described by all possible residual generators. The performance of the proposed identification method is tested by means of Monte Carlo simulations.
- Published
- 2006
24. Optimal errors-in-variables filtering in the mimo case
- Author
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ROBERTO DIVERSI, Guidorzi, R., Soverini, U., P. HORACEK, M. SIMANDL, P. ZITEK, R. Diversi, R. Guidorzi, and U. Soverini
- Subjects
DYNAMIC ERRORS-IN-VARIABLES MODELS ,RECURSIVE FILTERING ,OPTIMAL FILTERING ,LINEAR FILTERING - Abstract
The Errors-in-Variables (EIV) stochastic environment constitutes a superset of most common stochastic environments considered, for instance, in Kalman filtering or in equation-error identification here the process input is assumed as noise-free. Errors-in-variables models assume, on the contrary, the presence of unknown additive noise also on the inputs; the associated filtering procedures concern thus the optimal (minimal variance) estimation not only of the system state and output but also of the input. Optimal EIV filtering has been formulated and solved only recently making reference to SISO models; this paper extends the efficient algorithm proposed recently by the authors, based on the Cholesky factorization, to the more general multivariable case.
25. The Frisch scheme in algebraic and dynamic identification problems
- Author
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Guidorzi, R., ROBERTO DIVERSI, Soverini, U., R. Guidorzi, R. Diversi, and U. Soverini
- Subjects
ERRORS–IN–VARIABLES MODELS ,SYSTEM IDENTIFICATION ,FRISCH SCHEME ,LINEAR SYSTEMS - Abstract
This paper considers the problem of determining linear relations from data affected by additive noise in the context of the Frisch scheme. The loci of solutions of the Frisch scheme and their properties are first described in the algebraic case. In this context two main problems are analyzed: the evaluation of the maximal number of linear relations compatible with data affected by errors and the determination of the linear relation actually linking the noiseless data. Subsequently the extension of the Frisch scheme to the identification of dynamical systems is considered for both SISO and MIMO cases and the problem of its application to real processes is investigated. For this purpose suitable identification criteria and model parametrizations are described. Finally two classical identification problems are mapped into the Frisch scheme, the blind identification of FIR channels and the identification of AR + noise models. This allows some theoretical and practical extensions.
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