Journal ArticleDOI
A review of identification in continuous-time systems
Heinz Unbehauen,G.P. Rao +1 more
TLDR
A birds eye view of the continuous-time related aspects of the greater field of system identification is presented and some recent developments in the identification of linear systems and nonlinear systems are outlined.About:
This article is published in Annual Reviews in Control.The article was published on 1998-01-01. It has received 148 citations till now. The article focuses on the topics: Identification (information).read more
Citations
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Journal ArticleDOI
Parameter estimation in stochastic grey-box models
TL;DR: An efficient and flexible parameter estimation scheme for grey-box models in the sense of discretely, partially observed Ito stochastic differential equations with measurement noise is presented along with a corresponding software implementation that provides more accurate and more consistent estimates of the parameters of the diffusion term.
Journal ArticleDOI
Continuous-time model identification from sampled data: Implementation issues and performance evaluation
TL;DR: In this article, the authors deal 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.
Journal ArticleDOI
Identification of nonlinear systems using Polynomial Nonlinear State Space models
TL;DR: A method to model nonlinear systems using polynomial nonlinear state space equations by identifying first the best linear approximation of the system under test is proposed.
Journal ArticleDOI
Identification and estimation of continuous-time, data-based mechanistic (DBM) models for environmental systems
TL;DR: An introduction to the main aspects of existing time-domain methods for identifying linear continuous-time models from discrete-time data is provided and a widely applicable class of new, nonlinear, State Dependent Parameter (SDP) models are introduced.
Book
Sampled-Data Models for Linear and Nonlinear Systems
Yuz Eissmann,Juan Ignacio +1 more
TL;DR: Approximate Models for Linear Deterministic Systems and Applications of Approximate Sampled-data Models for Deterministic Nonlinear Systems in Estimation and Control.
References
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Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI
Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
Journal ArticleDOI
Time Series Analysis Forecasting and Control
TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Journal ArticleDOI
Identification and control of dynamical systems using neural networks
TL;DR: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems and the models introduced are practically feasible.