http://henrikmadsen.org/wp-content/uploads/2014/05/Journal_article_-_2004_-_Parameter_estimation_in_stochastic_grey-box_models.pdf

Parameter estimation in stochastic grey-box models

This paper deals with equation error methods that fit continuous-time transfer function models to discrete-time data recently included in the CONTSID (CONtinuous-Time System IDentification) Matlab toolbox. An overview of the methods is first given where implementation issues are highlighted. The performances of the methods are then evaluated on simulated examples by Monte Carlo simulations. The experiments have been carried out to study the sensitivity of each approach to the design parameters, sampling period, signal-to-noise ratio, noise power spectral density and type of input signal. The effectiveness of the CONTSID toolbox techniques is also briefly compared with indirect methods in which discrete-time models are first estimated and then transformed into continuous-time models. The paper does not consider iterative or recursive algorithms for continuous-time transfer function model identification.

Continuous-time model identification from sampled data: Implementation issues and performance evaluation

Identification of nonlinear systems using Polynomial Nonlinear State Space models

Initially, the paper provides an introduction to the main aspects of existing time-domain methods for identifying linear continuous-time models from discrete-time data and shows how one of these methods has been applied to the identification and estimation of a model for the transportation and dispersion of a pollutant in a river. It then introduces a widely applicable class of new, nonlinear, State Dependent Parameter (SDP) models. Finally, the paper describes how this SDP approach has been used to identify, estimate and control a nonlinear differential equation model of global carbon cycle dynamics and global warming.

/pdf/identification-and-estimation-of-continuous-time-data-based-34dxn72qew.pdf

Identification and estimation of continuous-time, data-based mechanistic (DBM) models for environmental systems

Part I: Deterministic Systems.- Background on Sampling of Signals.-Sampled Data Models for Linear Deterministic Systems.- Incremental Sampled Data Models.- Asymptotic Sampling Zeros.- Generalized Hold Devices.- Robustness.- Approximate Models for Linear Deterministic Systems.- Approximate Models for Deterministic Nonlinear Systems.- Applications of Approximate Sampled-data Models in Estimation and Control.- Part II: Stochastic Systems.- Background on Sampling of Stochastic Signals.- Sampled-data Models for Linear Stochastic Systems.- Incremental Stochastic Sampled-data Models.- Asymptotic Sampling Zeros for Linear Stochastic Systems.- Generalized Sampling Filters.- Approximate Sampled-data Models for Linear Stochastic Systems.- Stochastic Nonlinear Systems.- Approximate Sampled-data Models for Nonlinear Stochastic Systems.- Applications of Approximate Stochastic Sampled-data Models.- Part III: Embellishments and Extensions.- The Euler-Frobenius Polynominals.- Models for Intersample Response.- Approximate Sampled-data Models for Fractional Order Systems.

/pdf/sampled-data-models-for-linear-and-nonlinear-systems-rbckprenre.pdf

G.P. Rao

Papers

A review of identification in continuous-time systems