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Optimal Parameter Adaptive Estimation Of Stochastic Processes
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Download or read book Stochastic Systems written by P. R. Kumar and published by SIAM. This book was released on 2015-12-15 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with application in several branches of engineering and in those areas of the social sciences concerned with policy analysis and prescription. These approaches required a computing capacity too expensive for the time, until the ability to collect and process huge quantities of data engendered an explosion of work in the area. This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.
Book Synopsis Optimal Adaptive Estimation of Sampled Stochastic Processes by : Stanford University. Stanford Electronics Laboratories
Download or read book Optimal Adaptive Estimation of Sampled Stochastic Processes written by Stanford University. Stanford Electronics Laboratories and published by . This book was released on 1963 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents an adaptive approach to the problem of estimating a sampled, scalar-valued, stochastic process described by an initially unknown parameter vector. Knowledge of this quantity completely specifies the statistics of the proc ess, and consequently the optimal estimator must learn the value of the parameter vector. In order that construction of the optimal estimator be feasible it is necessary to consider only those processes whose parameter vector comes from a finite set of a priori known values. Fortunately, many practical problems may be represented or adequately approximated by such a model. The optimal estimator is found to be composed of a set of elemental estimators and a corresponding set of weighting coefficients, one pair for each possible value of the parameter vector. This structure is derived using properties of the conditional mean operator. For gauss-markov processes the elemental estimators are linear, dynamic systems, and evaluation of the weighting coefficients involves relatively simple, nonlinear calculations. The resulting system is optimum in the sense that it minimizes the expected value of a positive-definite, quadratic form in terms of the error (a generalized mean-square-error criterion). Because the system described in this work is optimal, it differs from previous attempts at adaptive estimation, all of which have used approximation techniques or suboptimal, sequential, optimization procedures. (Author).
Book Synopsis Scientific and Technical Aerospace Reports by :
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1991 with total page 1460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
Book Synopsis Stochastic Models: Estimation and Control: v. 2 by : Maybeck
Download or read book Stochastic Models: Estimation and Control: v. 2 written by Maybeck and published by Academic Press. This book was released on 1982-08-10 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Models: Estimation and Control: v. 2
Book Synopsis Proceedings of the 1975 IEEE Conference on Decision and Control Including the 14th Symposium on Adaptive Processes by :
Download or read book Proceedings of the 1975 IEEE Conference on Decision and Control Including the 14th Symposium on Adaptive Processes written by and published by . This book was released on 1975 with total page 920 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Linear Stochastic Systems by : Peter E. Caines
Download or read book Linear Stochastic Systems written by Peter E. Caines and published by SIAM. This book was released on 2018-06-12 with total page 892 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear Stochastic Systems, originally published in 1988, is today as comprehensive a reference to the theory of linear discrete-time-parameter systems as ever. Its most outstanding feature is the unified presentation, including both input-output and state space representations of stochastic linear systems, together with their interrelationships. The author first covers the foundations of linear stochastic systems and then continues through to more sophisticated topics including the fundamentals of stochastic processes and the construction of stochastic systems; an integrated exposition of the theories of prediction, realization (modeling), parameter estimation, and control; and a presentation of stochastic adaptive control theory. Written in a clear, concise manner and accessible to graduate students, researchers, and teachers, this classic volume also includes background material to make it self-contained and has complete proofs for all the principal results of the book. Furthermore, this edition includes many corrections of errata collected over the years.
Download or read book Proceedings written by and published by . This book was released on 1974 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Discrete Linear Control Systems by : V.N. Fomin
Download or read book Discrete Linear Control Systems written by V.N. Fomin and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: One service mathematics has rendered the 'Bt mm ... - si j'avait su comment en revenir, human race. It has put common sense back je n'y serais point alIe.' Jules Verne where it belongs. on the topmost shelf next to the dusty canister labelled 'discarded non The series is divergent; therefore we may be sense'. Eric T. Bell able to do something with it. O. Heavisidc Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .. .'; 'One service logic has rendered com puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.
Book Synopsis U.S. Government Research Reports by :
Download or read book U.S. Government Research Reports written by and published by . This book was released on 1964 with total page 1076 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis An Introduction to Identification by : J. P. Norton
Download or read book An Introduction to Identification written by J. P. Norton and published by Courier Corporation. This book was released on 2009-01-01 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Suitable for advanced undergraduates and graduate students, this text covers the theoretical basis for mathematical modeling as well as a variety of identification algorithms and their applications. 1986 edition.
Book Synopsis Statistical Applications in Process Control by : J. Bert Keats
Download or read book Statistical Applications in Process Control written by J. Bert Keats and published by CRC Press. This book was released on 1996-03-15 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents significant advances and new methods both in statistical process control and experimental design. It addresses the management of process monitoring and experimental design, discusses the relationship between control charting and hypothesis testing, provides a new index for process capability studies, offers practical guidelines for the design of experiments, and more.
Book Synopsis Control and Dynamic Systems V31: Advances in Aerospace Systems Dynamics and Control Systems Part 1 of 3 by : C.T. Leonides
Download or read book Control and Dynamic Systems V31: Advances in Aerospace Systems Dynamics and Control Systems Part 1 of 3 written by C.T. Leonides and published by Elsevier. This book was released on 2012-12-02 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control and Dynamic Systems: Advances in Theory in Applications, Volume 31: Advances in Aerospace Systems Dynamics and Control Systems, Part 1 of 3 deals with significant advances in technologies which support the development of aerospace systems. It also presents several algorithms and computational techniques used in complex aerospace systems. The techniques discussed in this volume include: moving-bank multiple model adaptive estimation, algorithms for multitarget sensor tracking systems; algorithms in differential dynamic programming; optimal control of linear stochastic systems; and normalized predictive deconvulation. This book is an important reference for practitioners in the field who want a comprehensive source of techniques with significant applied implications.
Book Synopsis Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control by : Ch. Venkateswarlu
Download or read book Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control written by Ch. Venkateswarlu and published by Elsevier. This book was released on 2022-01-31 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively leading to advances in this field. Design and implementation of various classical and advanced state estimation methods to solve a wide variety of problems makes this book immensely useful for the audience working in different disciplines in academics, research and industry in areas concerning to process monitoring, fault diagnosis, control and related disciplines. - Describes various classical and advanced versions of mechanistic model based state estimation algorithms - Describes various data-driven model based state estimation techniques - Highlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors - Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas
Book Synopsis Bayesian Analysis of Linear Models by : Broemeling
Download or read book Bayesian Analysis of Linear Models written by Broemeling and published by CRC Press. This book was released on 2017-11-22 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date source on the latest advances in thisfield has arisen.Presenting the basic theory of a large variety of linear models from a Bayesian viewpoint,Bayesian Analysis of Linear Models fills this need. Plus, this definitive volume containssomething traditional-a review of Bayesian techniques and methods of estimation, hypothesis,testing, and forecasting as applied to the standard populations ... somethinginnovative-a new approach to mixed models and models not generally studied by statisticianssuch as linear dynamic systems and changing parameter models ... and somethingpractical-clear graphs, eary-to-understand examples, end-of-chapter problems, numerousreferences, and a distribution appendix.Comprehensible, unique, and in-depth, Bayesian Analysis of Linear Models is the definitivemonograph for statisticians, econometricians, and engineers. In addition, this text isideal for students in graduate-level courses such as linear models, econometrics, andBayesian inference.
Book Synopsis Applied Control by : S. G. Tzafestas
Download or read book Applied Control written by S. G. Tzafestas and published by CRC Press. This book was released on 1993-04-29 with total page 1096 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a representative set of modern methodologies and applications, including new topics in the field, discussing a wide range of issues and treating them in depth. The book describes analytical processes for fault diagnosis of automatic control systems, examines modern sensors and actuators as well as measurement techniques, considers multidimensional feedback control and image restoration procedures, among other topics.
Book Synopsis Government Reports Announcements & Index by :
Download or read book Government Reports Announcements & Index written by and published by . This book was released on 1975 with total page 1258 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Identification and Stochastic Adaptive Control by : Han-fu Chen
Download or read book Identification and Stochastic Adaptive Control written by Han-fu Chen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Identifying the input-output relationship of a system or discovering the evolutionary law of a signal on the basis of observation data, and applying the constructed mathematical model to predicting, controlling or extracting other useful information constitute a problem that has been drawing a lot of attention from engineering and gaining more and more importance in econo metrics, biology, environmental science and other related areas. Over the last 30-odd years, research on this problem has rapidly developed in various areas under different terms, such as time series analysis, signal processing and system identification. Since the randomness almost always exists in real systems and in observation data, and since the random process is sometimes used to model the uncertainty in systems, it is reasonable to consider the object as a stochastic system. In some applications identification can be carried out off line, but in other cases this is impossible, for example, when the structure or the parameter of the system depends on the sample, or when the system is time-varying. In these cases we have to identify the system on line and to adjust the control in accordance with the model which is supposed to be approaching the true system during the process of identification. This is why there has been an increasing interest in identification and adaptive control for stochastic systems from both theorists and practitioners.
