Model Based Processing
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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.
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.
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 Reasoning About Learner Behaviour
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Author : Kees de Koning
language : en
Publisher: IOS Press
Release Date : 1997
Model Based Reasoning About Learner Behaviour written by Kees de Koning and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Computers categories.
Simulators are becoming standard equipment for interactive learning environments. They allow for attractive teaching with a large degree of freedom for the learner. However, without proper guidance, the learner easily gets lost in a simulation environment. Providing guidance requires an image of what the learner is doing. Acquiring this image by diagnosing the behaviour of the learner is a complex and resource-intensive task for which yet no general approach exists. In this book, we apply existing ideas and techniques from the field of model-based reasoning and diagnosis to interactive learning environments. We present a framework for subject matter modelling and diagnosis of learner behaviour. The framework defines generic techniques for automatically generating subject matter models from qualitative simulations. A generic model-based engine employs these models for diagnosing the learner's behaviour. The framework provides a powerful and reusable approach to individualising guidance in educational systems.
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 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.
Signal Processing
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Author : J.V. Candy
language : it
Publisher:
Release Date : 1987
Signal Processing written by J.V. Candy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with categories.
Model Based Development
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Author : H. S. Lahman
language : en
Publisher: Addison-Wesley Professional
Release Date : 2011
Model Based Development written by H. S. Lahman and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Application software categories.
A Proven Development Methodology That Delivers On the Promise of Model-Based Approaches Software continues to become more and more complex, while software consumers' expectations for performance, reliability, functionality, and speed-to-market are also growing exponentially. H. S. Lahman shows how to address all these challenges by integrating proven object-oriented techniques with a powerful new methodology. Model-Based Development represents Lahman's half century of experience as a pioneering software innovator. Building on Shlaer-Mellor's work, Lahman's unique approach fully delivers on the promise of models and is firmly grounded in the realities of contemporary development, design, and architecture. The book introduces the methodology's core principles, showing how it separates each of a project's concerns, enabling practitioners to optimize each domain for its unique needs and characteristics. Next, it demonstrates how to perform more effective object-oriented analysis, emphasizing abstraction, disciplined partitioning, modeling invariants, finite state machines, and efficient communications among program units. Coverage includes How we got here: a historical perspective and pragmatic review of object principles Problem space versus computing space: reflecting crucial distinctions between customer and computer environments in your designs Application partitioning: why it matters and how do it well Building static models that describe basic application structure Modeling classes, class responsibilities, associations, and both referential and knowledge integrity Creating dynamic models that describe behavior via finite state machines Successfully using abstract action languages (AALs) and action data flow diagrams (ADFDs) Throughout, Lahman illuminates theoretical issues in practical terms, explaining why things are done as they are, without demanding rigorous math. His focus is on creating implementation-independent models that resolve functional requirements completely, precisely, and unambiguously. Whether you're a developer, team leader, architect, or designer, Lahman's techniques will help you build software that's more robust, easier to maintain, supports larger-scale reuse, and whose specification is rigorous enough to enable full-scale automatic code generation.
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.
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.