Cover of: Discrete techniques of parameter estimation | Jerry M. Mendel Read Online
Share

Discrete techniques of parameter estimation the equation error formulation by Jerry M. Mendel

  • 379 Want to read
  • ·
  • 4 Currently reading

Published by M. Dekker in New York .
Written in English

Subjects:

  • Control theory.,
  • Parameter estimation.

Book details:

Edition Notes

Includes bibliographical references.

Statement[by] Jerry M. Mendel.
SeriesControl theory, v. 1
Classifications
LC ClassificationsQA402.3 .M397
The Physical Object
Paginationxiv, 385 p.
Number of Pages385
ID Numbers
Open LibraryOL5296177M
ISBN 100824714555
LC Control Number72076062

Download Discrete techniques of parameter estimation

PDF EPUB FB2 MOBI RTF

Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - Manufacturer: Marcel Drekker Inc. 3 Parameter Estimation Estimator:Statistic whose calculated value is used to estimate a parameter, θ Estimate:A particular realization of an estimator, θ Types of estimators: Point estimate: single number that can be regarded as the most plausible value of θ Interval estimate: a range of numbers, called a File Size: KB. Parameter estimation and discrete coded waveforms are also discussed, along with the effects of distortion on matched-filter signals. This book is comprised of 14 chapters and begins with an overview of the concepts and techniques of pulse compression matched filtering, with emphasis on coding source and decoding device. The famous book by Box and Jenkins (Box and Jenkins ) has had a substantial influence in many areas of engineering, but perhaps not as much in the control area, despite that it actually partly deals with control problems.

Filtering and parameter estimation techniques from Hidden Markov Models are then applied to obtain recursive estimates of the ‘drift’ and ‘volatility’. Further, all parameters in the model. Estimation in Discrete Parameter Models Christine Choirat and Raffaello Seri Abstract. Insome estimation problems,especially in applications deal-ing with information theory, signal processing and biology, theory pro-vides us with additional information allowing us to restrict the param-eter space to a finite number of by: Estimation in Discrete Parameter Models Article (PDF Available) in Statistical Science 27(2) February with 48 Reads How we measure 'reads'. Parameter Estimation and Inverse Problems, Third Edition, is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who do not have an extensive mathematical background. The book is complemented by a companion website that includes MATLAB codes that correspond to.

Prompted by recent developments in inverse theory, Inverse Problem Theory and Methods for Model Parameter Estimation is a completely rewritten version of a book by the same author. In this version there are lots of algorithmic details for Monte Carlo methods, least-squares discrete problems, and least-squares problems involving functions. extensive bibliography has not been available in the field of aircraft parameter estimation, and this document is the result of an effort to fill this void. The list is extensive, although not exhaustive, and does contain definitive works related to most aircraft parameter estimation approaches. TheoreticalCited by: 8. Parameter Estimation and Inverse Problems, Second Edition provides geoscience students and professionals with answers to common questions like how one can derive a physical model from a finite set of observations containing errors, and how one may determine the quality of such a model. This book takes on these fundamental and challenging problems, introducing students and professionals to the /5(7). Parameter estimation is a very difficult problem, especially for large systems, and a lot of effort This has led to the development of techniques determining which parameters affect the system’s dynamics the most, in order to choose the parameters M is the discrete model solution.