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Monday, July 6, 2020 | History

4 edition of Optimized system identification found in the catalog.

Optimized system identification

Optimized system identification

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Published by National Aeronautics and Space Administration, Langley Research Center, National Technical Information Service, distributor in Hampton, Va, [Springfield, Va .
Written in English


Edition Notes

StatementJer-Nan Juang, Richard W. Longman.
SeriesNASA/TM -- 1999-209711., NASA technical memorandum -- 209711.
ContributionsLongman, Richard W., Langley Research Center.
The Physical Object
FormatMicroform
Pagination1 v.
ID Numbers
Open LibraryOL17592572M
OCLC/WorldCa43451447

Stanley R. Liberty, in The Electrical Engineering Handbook, System Identification. In a system identification problem, one is normally asked to identify the mathematical input/output relation S from input/output data. In theory, this is straightforward if the system is truly linear (Chang, ), but in practice this may be difficult due to noisy data and finite precision . This book enables readers to understand system identification and linear system modeling through practical exercises without requiring complex theoretical knowledge. The contents encompass state-of-the-art system identification methods, with both time and frequency domain system identification methods covered, including the pros and cons of each.

Highlights Design of optimized periodic excitation signals (multisine, binary, three-level) Identification of continuous-time or discrete-time systems with unknown fractional delay Model order selection Model validation, including simulation, calculation of residuals, and test of cost functions Calculation of confidence intervals of amplitude/phase and poles/zeros. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda):: System Identification is the science of estimating models of dynamic systems by observing their inputoutput response. This paper provides a tutorial overview of the area with an emphasis on its relevance to using the estimated models for subsequent control system design.

Nonlinear control system identification is studied using neoteric optimized Least Squares Support Vector Machines (LS-SVM) in this paper. Firstly, a multi-layer adaptive optimizing parameters algorithm is developed for improving learning and generalization ability of least squares support vector machines. According to different learning problems, the optimization approach can .   / A system identification approach for improving behavioral interventions based on Social Cognitive Theory. Proceedings of the American Control Conference. Vol. July Institute of Electrical and Electronics Engineers Inc., pp.


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Optimized system identification Download PDF EPUB FB2

Optimized system identification (OCoLC) Microfiche version: Juang, Jer-Nan. Optimized system identification (OCoLC) Material Type: Document, Government publication, National government publication, Internet resource: Document Type: Internet Resource, Computer File: All Authors / Contributors.

OPTIMIZED SYSTEM IDENTIFICATION Jer-Nan Juang 1 NASA Langley Research Center Hampton, Virginia Richard W. Longman 2 Institute for Computer Applications in Science and Engineering NASA Langley Research Center Hampton, Virginia ABSTRACT In system identification, one usually cares most about finding a model whose outputs are as closeCited by: 9.

Optimized system identification (OCoLC) Online version: Juang, Jer-Nan. Optimized system identification (OCoLC) Material Type: Government publication, National government publication: Document Type: Book: All Authors / Contributors: Jer-Nan Juang; Richard W Longman; Langley Research Center.

This book is in all sense the angular stone for anyone who desire to start in a serious manner to develop skills in the System Identification subject. This book has all the options, the "theory for the user" are the "kitchen Optimized system identification book proper of the topic, this is the techniques that you can use without understand the background, and in the Cited by: The field of system identification uses statistical methods to build mathematical models of dynamical systems from measured data.

System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models as well as model reduction.

A common approach is to start from measurements of the behavior of the system. Linear system identification tools are useful if the dynamics of a system behave in a near-linear manner about a given operating condition, but cannot be modeled from first principles.

The System Identification Toolbox computes linear discrete and continuous models using both time and frequency domain data. Size: KB.

It is not easy to recommend the best reference for system identification for different levels of engineers and researchers. I summarize the comments based on. OPTIMIZED SYSTEM IDENTIFICATION Jer-Nan Juang1 NASA Langley Research Center Hampton, Virginia Richard W.

Longman2 Institute for Computer Applications in Science and Engineering NASA Langley Research Center Hampton, Virginia ABSTRACT In system identification, one usually cares most about finding a model whose outputs are as close. out of 5 stars Applied System Identification Book.

Reviewed in the United States on Ma The book has been well written. The author has a thorough understanding of the analytical approach combined with the real life experimental data. I have read over 10 different system identification books and none of those books even come Cited by: Abstract The normalized least-mean-square (NLMS) adaptive filter is widely used in system identification.

In this paper, we develop an optimized NLMS. System Identification: Theory for the User, 2e It gives the reader an understanding of available system identification methods and their rationales, properties, and uses.

This second edition introduces subspace methods, methods that utilize frequency-domain data, and general, nonlinear, black box methods including neural networks and neuro.

Principles of System Identification: Theory and Practice is an introductory-level book that presents the basic foundations and underlying methods relevant to system identification. The overall scope of the book focuses on system identification with an emphasis on practice, and concentrates most specifically on discrete-time linear system identification.

tific methods described in this book and obtained from statistics and system theory may help to solve the system identification problem in a systematic way. In gen-eral, system identification consists of three basic steps: experiment design and data acquisition, model structure selection and parameter estimation, and model vali-dation.

Optimized Systems is a specialty engineering and energy management firm that offers sensible, cost-saving solutions for conserving energy and improving building performance.

Whether you’re pondering a comprehensive energy strategy for your building or focusing on targeted, energy–saving measures, Optimized Systems will show you real. “Practical” Identification • Given: •Want 1) a model for the plant 2) a model for the noise 3) an estimate of the accuracy • choice of the model structureFile Size: 1MB.

In this book, different approaches for the identification of linear time-varying systems such as optimized forgetting factor approach, Kalman filter approach, combination of basis function and forgetting factor approach, and wavelet based approach have been presented.

Englisch. Industrial Use of System ID • Process control - most developed ID approaches – all plants and processes are different – need to do identification, cannot spend too much time on each – industrial identification tools • Aerospace – white-box identification, specially designed programs of tests • AutomotiveFile Size: KB.

Lennart Ljung's System Identification: Theory for the User is a complete, coherent description of the theory, methodology, and practice of System Identification. This completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data, and general non-linear black box methods, including neural networks and neuro-fuzzy modeling.

Signals used in system identification. The first step of system identification is the selection of input signals that will affect the system.

Step, square wave, sinusoidal wave, PRBS, impulse, pulse, and random signals are generally applied as inputs. Often, discrete time models are used to describe the system [8–11].

For this purpose Cited by: 1. Description. Appropriate for courses in System Identification. This book is a comprehensive and coherent description of the theory, methodology and practice of System Identification—the science of building mathematical models of dynamic systems by observing input/output puts the user in focus, giving the necessary background to understand theoretical foundation Format: Paper.

Book Description. Master Techniques and Successfully Build Models Using a Single Resource. Vital to all data-driven or measurement-based process operations, system identification is an interface that is based on observational science, and centers on developing mathematical models from observed data.The background required for the material in this book is relatively light if some discretion is exercised.

For the stationary system case, the presumed knowledge of linear system theory is not much beyond the typical third- or fourth-year undergraduate course that covers both state-equation and transfer-function concepts. However, a dose of the.System Identification: an Introduction shows the (student) reader how to approach the system identification problem in a systematic fashion.

Essentially, system identification is an art of modelling, where appropriate choices have to be made concerning the level of approximation, given prior system’s knowledge, noisy data and the final modelling objective.