The Experts below are selected from a list of 72966 Experts worldwide ranked by ideXlab platform

Samira Kamoun - One of the best experts on this subject based on the ideXlab platform.

  • a new Parametric Estimation algorithm for large scale systems described by state space mathematical models
    International Journal of Engineering, 2018
    Co-Authors: Samira Kamoun, M Kamoun
    Abstract:

    This paper is concerned with the Parametric Estimation problem for large-scale systems composed of several interconnected systems, which are described by linear discrete-time state-space mathematical models with unknown parameters. A new recursive Parametric Estimation algorithm with a particular adaptive gain is proposed for estimating the interconnected system parameters. The stability analysis of the developed Parametric Estimation scheme is treated by using the Lyapunov method. The proposed recursive Parametric Estimation algorithm is applied to estimate the parameters of a large-scale systems composed of three interconnected systems. The obtained numerical simulation results show the good performance of this algorithm.

  • Parametric Estimation of interconnected nonlinear systems described by input-output mathematical models
    International Journal of Automation and Computing, 2016
    Co-Authors: Mourad Elloumi, Samira Kamoun
    Abstract:

    In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive Estimation methods are used to solve the Parametric Estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the Parametric Estimation of a stochastic interconnected nonlinear hydraulic system is treated.

  • a recursive Parametric Estimation algorithm of multivariable nonlinear systems described by hammerstein mathematical models
    Applied Mathematical Modelling, 2015
    Co-Authors: Houda Salhi, Samira Kamoun
    Abstract:

    Abstract This paper aims at developing a Recursive Parametric Estimation (RPE) algorithm for Multi-Input Single-Output (MISO) and Multi-Input Multi-Output (MIMO) nonlinear systems, described by Hammerstein mathematical models. The problem formulation is achieved based on the adjustable model method and the least squares technique. The convergence analysis of the RPE algorithm is made using the Lyapunov method and his performance is illustrated using data from an experimental acid–base neutralization process.

  • State and Parametric Estimation of Nonlinear Systems Described by Wiener Sate-Space Mathematical Models
    Handbook of Research on Advanced Intelligent Control Engineering and Automation, 2015
    Co-Authors: Houda Salhi, Samira Kamoun
    Abstract:

    This chapter deals with the description, the Parametric Estimation, the state Estimation, and the Parametric and state Estimation conjointly of nonlinear systems. The focus is on the class of nonlinear systems, which are described by Wiener state-space discrete-time mathematical models. Thus, the authors develop a new recursive Parametric Estimation algorithm, which is based on least squares techniques. The stability conditions of the developed Parametric Estimation scheme are analyzed using the Lyapunov method. The state Estimation problem of the considered nonlinear systems is formulated. Thus, the authors propose a recursive state Estimation algorithm, which is based on Kalman Filter. A new recursive algorithm is proposed, which permits one to estimate conjointly the parameters and the state variables of nonlinear systems described by Wiener mathematical models, with unknown parameters and state variables. The efficiency and performance of the proposed recursive Estimation algorithms are tested on numerical simulation examples.

  • Design of a recursive Parametric Estimation algorithm for nonlinear system described by a Hammerstein mathematical model
    2013 International Conference on Electrical Engineering and Software Applications, 2013
    Co-Authors: Houda Salhi, Samira Kamoun
    Abstract:

    In this communication, a recursive Parametric Estimation algorithm is developed for the Parametric Estimation of nonlinear systems which can be described by a Hammerstein mathematical model. The formulation of this Parametric Estimation problem is made by using the method of the adjustable model and the least squares techniques. The stability analysis of the Parametric Estimation algorithm is made by using the Lyaponov theory. The performance of the developed recursive Parametric Estimation algorithm is illustrated using data from an experimental acid-base neutralization process.

Laurent Doyen - One of the best experts on this subject based on the ideXlab platform.

  • Semi-Parametric Estimation of Brown-Proschan preventive maintenance effects and intrinsic wear-out
    Computational Statistics and Data Analysis, 2014
    Co-Authors: Laurent Doyen
    Abstract:

    A system subject to corrective and preventive maintenance actions is considered. Corrective Maintenance (CM) is done at unpredictable random times and is assumed to have As Bad As Old (ABAO) effects. Preventive Maintenance (PM) is supposed to be done at deterministic predetermined times and to follow a Brown-Proschan (BP) model, i.e., each PM is As Good As New (AGAN) with probability p and ABAO with probability 1-p. In this context a semi-Parametric Estimation method is proposed: nonParametric Estimation of the first time to failure distribution and Parametric Estimation of the maintenance effect p. This work is original in considering that BP effects (ABAO or AGAN) are unknown or unobserved.

  • Semi-Parametric Estimation of Brown–Proschan preventive maintenance effects and intrinsic wear-out
    Computational Statistics & Data Analysis, 2014
    Co-Authors: Laurent Doyen
    Abstract:

    A system subject to corrective and preventive maintenance actions is considered. Corrective Maintenance (CM) is done at unpredictable random times and is assumed to have As Bad As Old (ABAO) effects. Preventive Maintenance (PM) is supposed to be done at deterministic predetermined times and to follow a Brown-Proschan (BP) model, i.e., each PM is As Good As New (AGAN) with probability p and ABAO with probability 1-p. In this context a semi-Parametric Estimation method is proposed: nonParametric Estimation of the first time to failure distribution and Parametric Estimation of the maintenance effect p. This work is original in considering that BP effects (ABAO or AGAN) are unknown or unobserved.

  • Semi-Parametric Estimation of imperfect preventive maintenance effects and intrinsic wear-out
    2013
    Co-Authors: Laurent Doyen
    Abstract:

    A major issue for industrial systems is the joint management of ageing and maintenance. An efficient maintenance and a controlled ageing allow the extension of the operating life on equipment. A system subject to corrective maintenance (CM) and preventive maintenance (PM) actions is considered. CM is carried out after failure. It is done at unpredictable random times. Its aim is to quickly restore the system in working order. Then, CM effects are assumed to be As Bad As Old (ABAO, i.e. leave the system in the state as it was before maintenance). PM are carried out when the system is operating and intend to slow down the wear process and reduce the frequency of occurrence of system failures. They are supposed to be done at predetermined times. Their effects are assumed to follow a Brown-Proschan (BP) model: each PM is As Good As New (AGAN, i.e. renews the system) with probability p and ABAO with probability (1-p). In this context a semi-Parametric Estimation method is proposed: non-Parametric Estimation of the first time to failure distribution and Parametric Estimation of the PM effect, p. The originality is to consider that BP effects (ABAO or AGAN) are unknown.

Houda Salhi - One of the best experts on this subject based on the ideXlab platform.

M Kamoun - One of the best experts on this subject based on the ideXlab platform.

Paolo Carbone - One of the best experts on this subject based on the ideXlab platform.

  • Cramér–Rao Lower Bound for Parametric Estimation of Quantized Sinewaves
    IEEE Transactions on Instrumentation and Measurement, 2007
    Co-Authors: Antonio Moschitta, Paolo Carbone
    Abstract:

    In this paper, the Crameacuter-Rao Lower Bound (CRLB) for the Parametric Estimation of quantized sinewaves is analyzed, including the effect of analog-to-digital-converter (ADC) noise. An accurate model based on the statistical properties of quantized data is presented, which takes into account both the effects of the finite ADC resolution and of the quantizer overloading phenomena at a low computational cost. Then, the model is validated by comparing the obtained results with the CRLB based on the ADC uniform noise model

  • Cramer-Rao lower bound for Parametric Estimation of quantized sinewaves
    Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510), 1
    Co-Authors: Antonio Moschitta, Paolo Carbone
    Abstract:

    In this paper, the Cramer-Rao Lower Bound (CRLB) for the Parametric Estimation of quantized sinewaves is analyzed, including the effect of ADC noise. An accurate model based on the statistical properties of quantized data is presented, which keeps into account both the effects of the finite ADC resolution and of quantizer overloading phenomena, at a low computational cost. Then, the model is validated by comparing obtained results with the CRLB based on the ADC uniform noise model.