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2 edition of Identification and system parameter estimation 1982 found in the catalog.

Identification and system parameter estimation 1982

IFAC Symposium on Identification and System Parameter Estimation (6th 1982 Washington)

Identification and system parameter estimation 1982

proceedings of the sixth IFAC symposium, Washington DC, USA, 7-11 June 1982

by IFAC Symposium on Identification and System Parameter Estimation (6th 1982 Washington)

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Published by Pergamon Press for the International Federation of Automatic Control in Oxford .
Written in English


Edition Notes

Sponsored by IFAC Technical Committee on Applications, IFAC Theory Committee, and organized by Control Systems Society of the Institute of Electrical and Electronics Engineers.

Statementedited by G. A. Bekey and G. N. Saridis. Vol. 1.
ContributionsBekey, G. A. 1928-, Saridis, G. N., International Federation of Automatic Control.
ID Numbers
Open LibraryOL20691458M

  Distributed Parameter Control Systems: Theory and Application is a two-part book consisting of 10 theoretical and five application-oriented chapters contributed by well-known workers in the distributed-parameter systems. The book covers topics of distributed parameter control systems in the areas of simulation, identification, state estimation Book Edition: 1.   (Phase I: model-free estimation) Substitute differentials with estimated slopes for each variable and at each time point (Voit, ; Voit and Almeida, ; Voit and Savageau, ) and construct a linear system of the form ‘Slope Vector(t) = [Stoichiometric Matrix] × [Flux Vector(t)]’, where the matrix is directly derived from the known (or hypothesized) topology of the by:

Prentice Hall. [This book gives a comprehensive overvie w of system identification including identifiability aspects.] Söderström T. and Stoica P. (). System Identification. pp. Hemel Hempstead, UK: Prentice-Hall International. [This book deals with parameter estimation methods including identifiability results.]. In econometrics and statistics, the generalized method of moments (GMM) is a generic method for estimating parameters in statistical y it is applied in the context of semiparametric models, where the parameter of interest is finite-dimensional, whereas the full shape of the data's distribution function may not be known, and therefore maximum likelihood estimation is not applicable.

  The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical Cited by:   System Identification. 'Prony's method' applies to dividing the multiple-parameter estimation problem into separate steps where each parameter is estimated independently from the others (from a numerical sense). The book I tried to hunt down 15 years ago (and never found except in the university library) was his book on spectrum.


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Identification and system parameter estimation 1982 by IFAC Symposium on Identification and System Parameter Estimation (6th 1982 Washington) Download PDF EPUB FB2

Identification and System Parameter Estimation covers the proceedings of the Sixth International Federation of Automatic Control (IFAC) Symposium. The book also serves as a tribute to Dr. Naum S. Edition: 1. Get this from a library. Identification and system parameter estimation proceedings of the Sixth IFAC Symposium, Washington DC, USA, June [George A Bekey; George N Saridis; IEEE Control Systems Society.;].

Identification and System Parameter Estimation Proceedings of the Sixth IFAC Symposium, Washington DC, USA, June Flight Vehicle System Identification, Second Edition offers a systematic approach to flight vehicle system identification and covers exhaustively the time-domain methodology.

Beginners, as well as practicing engineers, researchers, and working professionals who wish to refresh or broaden their knowledge of flight vehicle system identification, will find this book highly beneficial.

The design of parameter-adaptive control algorithms which are based on proper parameter estimation and linear control algorithms is considered for single-input single-output processes. [Golub, ] is extended to cope with parameter identification of dynamical systems with multiple inputs and outputs.

Software for Computer Control Control > Vol. 28, No. 3, Digital Identification of Continuous-Time System Parameters8. Hung and R.

Liu, "A Comparison of Several Algebraic Methods for Con trol System Applications," Proceed ings of IEEE Southeastcon, IEEE Cited by: 1. Hamel, P.G., Aircraft parameter identification methods and their applications–Survey and future aspects- in AGARD Lecture Series No.

Parameter Identification, AGARD, London,1–1. Google ScholarCited by: 1. Description This valuable volume offers a systematic approach to flight vehicle system identification and exhaustively covers the time domain methodology.

It addresses in detail the theoretical and practical aspects of various parameter estimation methods, including those in the stochastic framework and focusing on nonlinear models, cost. Lai T.L. and C.Z. Wei (), Least squares estimation in stochastic regression models with application to identification and control of dynamic systems, The Annals of Statistics, 10, – Google ScholarCited by: System identification is concerned with the estimation of a system on the basis of observed data.

This involves specification of the model structure, estimation of the unknown model parameters. and does contain definitive works related to most aircraft parameter estimation approaches. Theoretical studies as well as practical applications are included.

Many of these publications are pertinent to subjects peripherally related to parameter estimation, such as aircraft maneuver design or instrumenta- Cited by: 8. Identification of Linear Structural Dynamic Systems.

This paper studies methods of parameter estimation for linear multi-degree-of-freedom structural dynamic systems, based on observed records of the external forces and the structural responses. The auto-regressive and moving-average (ARMA) model is used for this purpose.

It is found that the ARMA model is a convenient model representing Cited by: Search text. Search type Research Explorer Website Staff directory. Alternatively, use our A–Z indexAuthor: Carlo { noopsort{proc g}}{ Berzuini, Silvana Quaglini, Mario Stefanelli.

design and parameter estimation in the overall identification procedure. The study of optimal experiments for parameter estimation is of interest in a number of areas of research, e.g.

heavy industry, process control, chemical engineering, etc., where the final goal of identification is the determination of specific parameter values. Koivo and H. Koivo,"Control and Estimation in Systems with Time Delay," Chapter 5 in Identification, Estimation and Control of Distributed in Health Care, Philadelphia, PA, September [51] A.

Koivo and Adaptive Controller," 6th IFAC Symposium on Identification and System Parameter Estimation, Washington, D.C., June. ISBN ; Free shipping for individuals worldwide; Immediate ebook access* with your print order; Usually dispatched within 3 to 5 business days.

2 Time-Domain Identification of Linear System Matrices The use of least-squares methods to estimate unknown parameters is a well known and developed approach which oc- cupies significant portions of numerous books devoted to the subject of parameter estimation, particularly in the field ofFile Size: KB.

Parameter identification problems for delay systems motivated by examples from aerody- namics and biochemistry are considered. The problem of estimation of the delays is included.

Using approximati Cited by: The paper focuses on a novel bias-free least squares (BFLS) parameter estimation approach which has received attention in the system identification and control : Wei Xing Zheng.

[A special issue entirely dedicated to bound-based identification] Walter E. and Pronzato L. Identification of Parametric Models from Experimental Data, pp. London: Springer-Verlag.

[Bound-based identification is just one of the topics considered, in part of Chapter 5, devoted to the quantification of parameter uncertainty]. () Identification of compartmental models for perturbed cell populations using state-space parameter estimation techniques. International Journal of Bio-Medical Computing() Identification of moderately nonlinear flight mechanics systems Cited by: "For contributions to parameter robustness analysis of state space systems and robust control of linear uncertain systems" Kemin Zhou "For contributions to the robust control system theory and applications" Er-wei Bai "For contributions to system .2.

Stabilization of Distributed Parameter Systems by Boundary Feedback 3. System Iodelling and Identification 4. Control of Flexible Flight Vehicles 5. Random Fields: Filtering and Estimation 6.

Control of Randomly Varying Systems 7. Control of Large Space Structures The progress made on each of them is described below under the corresponding.