Model Based Processing
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Model Based Signal Processing
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Author : James V. Candy
language : en
Publisher: John Wiley & Sons
Release Date : 2005-10-27
Model Based Signal Processing written by James V. Candy and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-10-27 with Technology & Engineering categories.
A unique treatment of signal processing using a model-based perspective Signal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels require using the underlying physics to correct the problem causing these low levels and extracting the desired information. Model-based signal processing incorporates the physical phenomena, measurements, and noise in the form of mathematical models to solve this problem. Not only does the approach enable signal processors to work directly in terms of the problem's physics, instrumentation, and uncertainties, but it provides far superior performance over the standard techniques. Model-based signal processing is both a modeler's as well as a signal processor's tool. Model-Based Signal Processing develops the model-based approach in a unified manner and follows it through the text in the algorithms, examples, applications, and case studies. The approach, coupled with the hierarchy of physics-based models that the author develops, including linear as well as nonlinear representations, makes it a unique contribution to the field of signal processing. The text includes parametric (e.g., autoregressive or all-pole), sinusoidal, wave-based, and state-space models as some of the model sets with its focus on how they may be used to solve signal processing problems. Special features are provided that assist readers in understanding the material and learning how to apply their new knowledge to solving real-life problems. * Unified treatment of well-known signal processing models including physics-based model sets * Simple applications demonstrate how the model-based approach works, while detailed case studies demonstrate problem solutions in their entirety from concept to model development, through simulation, application to real data, and detailed performance analysis * Summaries provided with each chapter ensure that readers understand the key points needed to move forward in the text as well as MATLAB(r) Notes that describe the key commands and toolboxes readily available to perform the algorithms discussed * References lead to more in-depth coverage of specialized topics * Problem sets test readers' knowledge and help them put their new skills into practice The author demonstrates how the basic idea of model-based signal processing is a highly effective and natural way to solve both basic as well as complex processing problems. Designed as a graduate-level text, this book is also essential reading for practicing signal-processing professionals and scientists, who will find the variety of case studies to be invaluable. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department
Model Based Processing
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Author : James V. Candy
language : en
Publisher: John Wiley & Sons
Release Date : 2019-03-15
Model Based Processing written by James V. Candy and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-15 with Technology & Engineering categories.
A bridge between the application of subspace-based methods for parameter estimation in signal processing and subspace-based system identification in control systems Model-Based Processing: An Applied Subspace Identification Approach provides expert insight on developing models for designing model-based signal processors (MBSP) employing subspace identification techniques to achieve model-based identification (MBID) and enables readers to evaluate overall performance using validation and statistical analysis methods. Focusing on subspace approaches to system identification problems, this book teaches readers to identify models quickly and incorporate them into various processing problems including state estimation, tracking, detection, classification, controls, communications, and other applications that require reliable models that can be adapted to dynamic environments. The extraction of a model from data is vital to numerous applications, from the detection of submarines to determining the epicenter of an earthquake to controlling an autonomous vehicles—all requiring a fundamental understanding of their underlying processes and measurement instrumentation. Emphasizing real-world solutions to a variety of model development problems, this text demonstrates how model-based subspace identification system identification enables the extraction of a model from measured data sequences from simple time series polynomials to complex constructs of parametrically adaptive, nonlinear distributed systems. In addition, this resource features: Kalman filtering for linear, linearized, and nonlinear systems; modern unscented Kalman filters; as well as Bayesian particle filters Practical processor designs including comprehensive methods of performance analysis Provides a link between model development and practical applications in model-based signal processing Offers in-depth examination of the subspace approach that applies subspace algorithms to synthesized examples and actual applications Enables readers to bridge the gap from statistical signal processing to subspace identification Includes appendices, problem sets, case studies, examples, and notes for MATLAB Model-Based Processing: An Applied Subspace Identification Approach is essential reading for advanced undergraduate and graduate students of engineering and science as well as engineers working in industry and academia.
Model Based Processing For Underwater Acoustic Arrays
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Author : Edmund J. Sullivan
language : en
Publisher: Springer
Release Date : 2015-05-14
Model Based Processing For Underwater Acoustic Arrays written by Edmund J. Sullivan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-14 with Science categories.
This monograph presents a unified approach to model-based processing for underwater acoustic arrays. The use of physical models in passive array processing is not a new idea, but it has been used on a case-by-case basis, and as such, lacks any unifying structure. This work views all such processing methods as estimation procedures, which then can be unified by treating them all as a form of joint estimation based on a Kalman-type recursive processor, which can be recursive either in space or time, depending on the application. This is done for three reasons. First, the Kalman filter provides a natural framework for the inclusion of physical models in a processing scheme. Second, it allows poorly known model parameters to be jointly estimated along with the quantities of interest. This is important, since in certain areas of array processing already in use, such as those based on matched-field processing, the so-called mismatch problem either degrades performance or, indeed, prevents any solution at all. Thirdly, such a unification provides a formal means of quantifying the performance improvement. The term model-based will be strictly defined as the use of physics-based models as a means of introducing a priori information. This leads naturally to viewing the method as a Bayesian processor. Short expositions of estimation theory and acoustic array theory are presented, followed by a presentation of the Kalman filter in its recursive estimator form. Examples of applications to localization, bearing estimation, range estimation and model parameter estimation are provided along with experimental results verifying the method. The book is sufficiently self-contained to serve as a guide for the application of model-based array processing for the practicing engineer.
Model Based Process Control
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Author : T.J. McAvoy
language : en
Publisher: Elsevier
Release Date : 2014-06-28
Model Based Process Control written by T.J. McAvoy and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
Presented at this workshop were mathematical models upon which process control is based and the practical applications of this method of control within industry; case studies include examples from the paper and pulp industry, materials industry and the chemical industry, among others. From these presentations emerged a need for further research and development into process control. Containing 19 papers these Proceedings will be a valuable reference work for all those involved in the designing of continuous production processes for industry and for the end user involved in the practical application of process control within their manufacturing process.
Nonlinear Model Based Process Control
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Author : Rashid M. Ansari
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Nonlinear Model Based Process Control written by Rashid M. Ansari and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The last decade has seen considerable interest in reviving the fortunes of non linear control. In contrast to the approaches of the 60S, 70S and 80S a very pragmatic agenda for non-linear control is being pursued using the model-based predictive control paradigm. This text by R. Ansari and M. Tade gives an excellent synthesis of this new direction. Two strengths emphasized by the text are: (i) four applications found in refinery processes are used to give the text a firm practical continuity; (ii) a non-linear model-based control architecture is used to give the method a coherent theoretical framework.
Signal Processing
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Author : James V. Candy
language : en
Publisher: McGraw-Hill Companies
Release Date : 1986
Signal Processing written by James V. Candy and has been published by McGraw-Hill Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with Technology & Engineering categories.
Methods Of Model Based Process Control
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Author : R. Berber
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Methods Of Model Based Process Control written by R. Berber and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
Model based control has emerged as an important way to improve plant efficiency in the process industries, while meeting processing and operating policy constraints. The reader of Methods of Model Based Process Control will find state of the art reports on model based control technology presented by the world's leading scientists and experts from industry. All the important issues that a model based control system has to address are covered in depth, ranging from dynamic simulation and control-relevant identification to information integration. Specific emerging topics are also covered, such as robust control and nonlinear model predictive control. In addition to critical reviews of recent advances, the reader will find new ideas, industrial applications and views of future needs and challenges. Audience: A reference for graduate-level courses and a comprehensive guide for researchers and industrial control engineers in their explorationof the latest trends in the area.
Nonlinear Model Based Process Control
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Author : R. Berber
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Nonlinear Model Based Process Control written by R. Berber and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
The ASI on Nonlinear Model Based Process Control (August 10-20, 1997~ Antalya - Turkey) convened as a continuation of a previous ASI which was held in August 1994 in Antalya on Methods of Model Based Process Control in a more general context. In 1994, the contributions and discussions convincingly showed that industrial process control would increasingly rely on nonlinear model based control systems. Therefore, the idea for organizing this ASI was motivated by the success of the first one, the enthusiasm expressed by the scientific community for continuing contact, and the growing incentive for on-line control algorithms for nonlinear processes. This is due to tighter constraints and constantly changing performance objectives that now force the processes to be operated over a wider range of conditions compared to the past, and the fact that many of industrial operations are nonlinear in nature. The ASI intended to review in depth and in a global way the state-of-the-art in nonlinear model based control. The list of lecturers consisted of 12 eminent scientists leading the principal developments in the area, as well as industrial specialists experienced in the application of these techniques. Selected out of a large number of applications, there was a high quality, active audience composed of 59 students from 20 countries. Including family members accompanying the participants, the group formed a large body of92 persons. Out of the 71 participants, 11 were from industry.
Nonlinear Model Based Process Control
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Author : Rıdvan Berber
language : en
Publisher: Springer Science & Business Media
Release Date : 1998
Nonlinear Model Based Process Control written by Rıdvan Berber and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Computers categories.
The increasingly competitive environment within which modern industry has to work means that processes have to be operated over a wider range of conditions in order to meet constantly changing performance targets. Add to this the fact that many industrial operations are nonlinear, and the need for on-line control algorithms for nonlinear processes becomes clear. Major progress has been booked in constrained model-based control and important issues of nonlinear process control have been solved. This text surveys the state-of-the-art in nonlinear model-based control technology, by writers who have actually created the scientific profile. A broad range of issues are covered in depth, from traditional nonlinear approaches to nonlinear model predictive control, from nonlinear process identification and state estimation to control-integrated design. Advances in the control of inverse response and unstable processes are presented. Comparisons with linear control are given, and case studies are used for illustration.
Model Based Process Supervision
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Author : Arun Kumar Samantaray
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-03-14
Model Based Process Supervision written by Arun Kumar Samantaray and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-14 with Technology & Engineering categories.
This book provides control engineers and workers in industrial and academic research establishments interested in process engineering with a means to build up a practical and functional supervisory control environment and to use sophisticated models to get the best use out of their process data. Several applications to academic and small-scale-industrial processes are discussed and the development of a supervision platform for an industrial plant is presented.