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Author : Patrick John Donoghue
Publisher :
ISBN 13 :
Total Pages : 226 pages
Book Rating : 4.3/5 (129 download)
Download or read book System Identification by Bayesian Learning written by Patrick John Donoghue and published by . This book was released on 1968 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Ke Huang
Publisher : Springer Nature
ISBN 13 : 9819905931
Total Pages : 286 pages
Book Rating : 4.8/5 (199 download)
Download or read book Bayesian Real-Time System Identification written by Ke Huang and published by Springer Nature. This book was released on 2023-03-20 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces some recent developments in Bayesian real-time system identification. It contains two different perspectives on data processing for system identification, namely centralized and distributed. A centralized Bayesian identification framework is presented to address challenging problems of real-time parameter estimation, which covers outlier detection, system, and noise parameters tracking. Besides, real-time Bayesian model class selection is introduced to tackle model misspecification problem. On the other hand, a distributed Bayesian identification framework is presented to handle asynchronous data and multiple outlier corrupted data. This book provides sufficient background to follow Bayesian methods for solving real-time system identification problems in civil and other engineering disciplines. The illustrative examples allow the readers to quickly understand the algorithms and associated applications. This book is intended for graduate students and researchers in civil and mechanical engineering. Practitioners can also find useful reference guide for solving engineering problems.
Author : Pieter Eykhoff
Publisher : Elsevier
ISBN 13 : 1483148661
Total Pages : 419 pages
Book Rating : 4.4/5 (831 download)
Download or read book Trends and Progress in System Identification written by Pieter Eykhoff and published by Elsevier. This book was released on 2014-05-20 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Trends and Progress in System Identification is a three-part book that focuses on model considerations, identification methods, and experimental conditions involved in system identification. Organized into 10 chapters, this book begins with a discussion of model method in system identification, citing four examples differing on the nature of the models involved, the nature of the fields, and their goals. Subsequent chapters describe the most important aspects of model theory; the ""classical"" methods and time series estimation; application of least squares and related techniques for the estimation of dynamic system parameters; the maximum likelihood and error prediction methods; and the modern development of statistical methods. Non-parametric approaches, identification of nonlinear systems by piecewise approximation, and the minimax identification are then explained. Other chapters explore the Bayesian approach to system identification; choice of input signals; and choice and effect of different feedback configurations in system identification. This book will be useful for control engineers, system scientists, biologists, and members of other disciplines dealing withdynamical relations.
Author : Ka-Veng Yuen
Publisher : John Wiley & Sons
ISBN 13 : 9780470824559
Total Pages : 320 pages
Book Rating : 4.8/5 (245 download)
Download or read book Bayesian Methods for Structural Dynamics and Civil Engineering written by Ka-Veng Yuen and published by John Wiley & Sons. This book was released on 2010-02-22 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian methods are a powerful tool in many areas of science and engineering, especially statistical physics, medical sciences, electrical engineering, and information sciences. They are also ideal for civil engineering applications, given the numerous types of modeling and parametric uncertainty in civil engineering problems. For example, earthquake ground motion cannot be predetermined at the structural design stage. Complete wind pressure profiles are difficult to measure under operating conditions. Material properties can be difficult to determine to a very precise level – especially concrete, rock, and soil. For air quality prediction, it is difficult to measure the hourly/daily pollutants generated by cars and factories within the area of concern. It is also difficult to obtain the updated air quality information of the surrounding cities. Furthermore, the meteorological conditions of the day for prediction are also uncertain. These are just some of the civil engineering examples to which Bayesian probabilistic methods are applicable. Familiarizes readers with the latest developments in the field Includes identification problems for both dynamic and static systems Addresses challenging civil engineering problems such as modal/model updating Presents methods applicable to mechanical and aerospace engineering Gives engineers and engineering students a concrete sense of implementation Covers real-world case studies in civil engineering and beyond, such as: structural health monitoring seismic attenuation finite-element model updating hydraulic jump artificial neural network for damage detection air quality prediction Includes other insightful daily-life examples Companion website with MATLAB code downloads for independent practice Written by a leading expert in the use of Bayesian methods for civil engineering problems This book is ideal for researchers and graduate students in civil and mechanical engineering or applied probability and statistics. Practicing engineers interested in the application of statistical methods to solve engineering problems will also find this to be a valuable text. MATLAB code and lecture materials for instructors available at http://www.wiley.com/go/yuen
Author : J. Schoukens
Publisher : Elsevier
ISBN 13 : 0080912567
Total Pages : 353 pages
Book Rating : 4.0/5 (89 download)
Download or read book Identification of Linear Systems written by J. Schoukens and published by Elsevier. This book was released on 2014-06-28 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book concentrates on the problem of accurate modeling of linear systems. It presents a thorough description of a method of modeling a linear dynamic invariant system by its transfer function. The first two chapters provide a general introduction and review for those readers who are unfamiliar with identification theory so that they have a sufficient background knowledge for understanding the methods described later. The main body of the book looks at the basic method used by the authors to estimate the parameter of the transfer function, how it is possible to optimize the excitation signals. Further chapters extend the estimation method proposed. Applications are then discussed and the book concludes with practical guidelines which illustrate the method and offer some rules-of-thumb.
Author : Tony L. Schmitz
Publisher : Springer
ISBN 13 : 3319937073
Total Pages : 389 pages
Book Rating : 4.3/5 (199 download)
Download or read book Machining Dynamics written by Tony L. Schmitz and published by Springer. This book was released on 2018-10-30 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book trains engineers and students in the practical application of machining dynamics, with a particular focus on milling. The book walks readers through the steps required to improve machining productivity through chatter avoidance and reduced surface location error, and covers in detail topics such as modal analysis (including experimental methods) to obtain the tool point frequency response function, descriptions of turning and milling, force modeling, time domain simulation, stability lobe diagram algorithms, surface location error calculation for milling, beam theory, and more. This new edition includes updates throughout the entire text, new exercises and examples, and a new chapter on machining tribology. It is a valuable resource for practicing manufacturing engineers and graduate students interested in learning how to improve machining productivity through consideration of the process dynamics.
Author : Wenhui Fan
Publisher : Springer Nature
ISBN 13 : 9811991987
Total Pages : 648 pages
Book Rating : 4.8/5 (119 download)
Download or read book Methods and Applications for Modeling and Simulation of Complex Systems written by Wenhui Fan and published by Springer Nature. This book was released on 2022-12-22 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set CCIS 1712 and 1713 constitutes the proceedings of the 21st Asian Simulation Conference, AsiaSim 2022, which took place in Changsha, China, in January 2023. Due to the Covid pandemic AsiaSim 2022 has been postponed to January 2023. The 97 papers presented in the proceedings were carefully reviewed and selected from 218 submissions. The contributions were organized in topical sections as follows: Modeling theory and methodology; Continuous system/discrete event system/hybrid system/intelligent system modeling and simulation; Complex systems and open, complex and giant systems modeling and simulation; Integrated natural environment and virtual reality environment modeling and simulation; Networked Modeling and Simulation; Flight simulation, simulator, simulation support environment, simulation standard and simulation system construction; High performance computing, parallel computing, pervasive computing, embedded computing and simulation; CAD/CAE/CAM/CIMS/VP/VM/VR/SBA; Big data challenges and requirements for simulation and knowledge services of big data ecosystem; Artificial intelligence for simulation; Application of modeling/simulation in science/engineering/society/economy /management/energy/transportation/life/biology/medicine etc; Application of modeling/simulation in energy saving/emission reduction, public safety, disaster prevention/mitigation; Modeling/simulation applications in the military field; Modeling/simulation applications in education and training; Modeling/simulation applications in entertainment and sports.
Author : Devinderjit Sivia
Publisher : OUP Oxford
ISBN 13 : 0191546704
Total Pages : 259 pages
Book Rating : 4.1/5 (915 download)
Download or read book Data Analysis written by Devinderjit Sivia and published by OUP Oxford. This book was released on 2006-06-02 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples. - Katie St. Clair MAA Reviews.
Author : Le Yi Wang
Publisher : Springer Science & Business Media
ISBN 13 : 0817649565
Total Pages : 317 pages
Book Rating : 4.8/5 (176 download)
Download or read book System Identification with Quantized Observations written by Le Yi Wang and published by Springer Science & Business Media. This book was released on 2010-05-18 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recently developed methodologies that utilize quantized information in system identification and explores their potential in extending control capabilities for systems with limited sensor information or networked systems. The results of these methodologies can be applied to signal processing and control design of communication and computer networks, sensor networks, mobile agents, coordinated data fusion, remote sensing, telemedicine, and other fields in which noise-corrupted quantized data need to be processed. System Identification with Quantized Observations is an excellent resource for graduate students, systems theorists, control engineers, applied mathematicians, as well as practitioners who use identification algorithms in their work.
Author : Shinichi Nakajima
Publisher : Cambridge University Press
ISBN 13 : 1107076153
Total Pages : 561 pages
Book Rating : 4.1/5 (7 download)
Download or read book Variational Bayesian Learning Theory written by Shinichi Nakajima and published by Cambridge University Press. This book was released on 2019-07-11 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: This introduction to the theory of variational Bayesian learning summarizes recent developments and suggests practical applications.
Author : Arun K. Tangirala
Publisher : CRC Press
ISBN 13 : 143989602X
Total Pages : 908 pages
Book Rating : 4.4/5 (398 download)
Download or read book Principles of System Identification written by Arun K. Tangirala and published by CRC Press. This book was released on 2018-10-08 with total page 908 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. 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. Useful for Both Theory and Practice The book presents the foundational pillars of identification, namely, the theory of discrete-time LTI systems, the basics of signal processing, the theory of random processes, and estimation theory. It explains the core theoretical concepts of building (linear) dynamic models from experimental data, as well as the experimental and practical aspects of identification. The author offers glimpses of modern developments in this area, and provides numerical and simulation-based examples, case studies, end-of-chapter problems, and other ample references to code for illustration and training. Comprising 26 chapters, and ideal for coursework and self-study, this extensive text: Provides the essential concepts of identification Lays down the foundations of mathematical descriptions of systems, random processes, and estimation in the context of identification Discusses the theory pertaining to non-parametric and parametric models for deterministic-plus-stochastic LTI systems in detail Demonstrates the concepts and methods of identification on different case-studies Presents a gradual development of state-space identification and grey-box modeling Offers an overview of advanced topics of identification namely the linear time-varying (LTV), non-linear, and closed-loop identification Discusses a multivariable approach to identification using the iterative principal component analysis Embeds MATLAB® codes for illustrated examples in the text at the respective points Principles of System Identification: Theory and Practice presents a formal base in LTI deterministic and stochastic systems modeling and estimation theory; it is a one-stop reference for introductory to moderately advanced courses on system identification, as well as introductory courses on stochastic signal processing or time-series analysis.The MATLAB scripts and SIMULINK models used as examples and case studies in the book are also available on the author's website: http://arunkt.wix.com/homepage#!textbook/c397
Author : Paul Gustafson
Publisher : CRC Press
ISBN 13 : 9780367570538
Total Pages : 196 pages
Book Rating : 4.5/5 (75 download)
Download or read book Bayesian Inference for Partially Identified Models written by Paul Gustafson and published by CRC Press. This book was released on 2020-06-30 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how the Bayesian approach to inference is applicable to partially identified models (PIMs) and examines the performance of Bayesian procedures in partially identified contexts. Drawing on his many years of research in this area, the author presents a thorough overview of the statistical theory, properties, and applications of PIM
Author : Matthew R.W. Brake
Publisher : Springer Nature
ISBN 13 : 3031369998
Total Pages : 257 pages
Book Rating : 4.0/5 (313 download)
Download or read book Nonlinear Structures & Systems, Volume 1 written by Matthew R.W. Brake and published by Springer Nature. This book was released on 2023-11-14 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear Structures & Systems, Volume 1: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the first volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Nonlinear Dynamics, including papers on: Experimental Nonlinear Dynamics Jointed Structures: Identification, Mechanics, Dynamics Nonlinear Damping Nonlinear Modeling and Simulation Nonlinear Reduced-Order Modeling Nonlinearity and System Identification
Author : Stephen A. Billings
Publisher : John Wiley & Sons
ISBN 13 : 1118535553
Total Pages : 611 pages
Book Rating : 4.1/5 (185 download)
Download or read book Nonlinear System Identification written by Stephen A. Billings and published by John Wiley & Sons. This book was released on 2013-07-29 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model The orthogonal least squares algorithm that allows models to be built term by term where the error reduction ratio reveals the percentage contribution of each model term Statistical and qualitative model validation methods that can be applied to any model class Generalised frequency response functions which provide significant insight into nonlinear behaviours A completely new class of filters that can move, split, spread, and focus energy The response spectrum map and the study of sub harmonic and severely nonlinear systems Algorithms that can track rapid time variation in both linear and nonlinear systems The important class of spatio-temporal systems that evolve over both space and time Many case study examples from modelling space weather, through identification of a model of the visual processing system of fruit flies, to tracking causality in EEG data are all included to demonstrate how easily the methods can be applied in practice and to show the insight that the algorithms reveal even for complex systems NARMAX algorithms provide a fundamentally different approach to nonlinear system identification and signal processing for nonlinear systems. NARMAX methods provide models that are transparent, which can easily be analysed, and which can be used to solve real problems. This book is intended for graduates, postgraduates and researchers in the sciences and engineering, and also for users from other fields who have collected data and who wish to identify models to help to understand the dynamics of their systems.
Author : Gianluigi Pillonetto
Publisher : Springer Nature
ISBN 13 : 3030958604
Total Pages : 394 pages
Book Rating : 4.0/5 (39 download)
Download or read book Regularized System Identification written by Gianluigi Pillonetto and published by Springer Nature. This book was released on 2022-05-13 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book.
Author : Diana Cole
Publisher : CRC Press
ISBN 13 : 1498720900
Total Pages : 273 pages
Book Rating : 4.4/5 (987 download)
Download or read book Parameter Redundancy and Identifiability written by Diana Cole and published by CRC Press. This book was released on 2020-05-10 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical and mathematical models are defined by parameters that describe different characteristics of those models. Ideally it would be possible to find parameter estimates for every parameter in that model, but, in some cases, this is not possible. For example, two parameters that only ever appear in the model as a product could not be estimated individually; only the product can be estimated. Such a model is said to be parameter redundant, or the parameters are described as non-identifiable. This book explains why parameter redundancy and non-identifiability is a problem and the different methods that can be used for detection, including in a Bayesian context. Key features of this book: Detailed discussion of the problems caused by parameter redundancy and non-identifiability Explanation of the different general methods for detecting parameter redundancy and non-identifiability, including symbolic algebra and numerical methods Chapter on Bayesian identifiability Throughout illustrative examples are used to clearly demonstrate each problem and method. Maple and R code are available for these examples More in-depth focus on the areas of discrete and continuous state-space models and ecological statistics, including methods that have been specifically developed for each of these areas This book is designed to make parameter redundancy and non-identifiability accessible and understandable to a wide audience from masters and PhD students to researchers, from mathematicians and statisticians to practitioners using mathematical or statistical models.
Author : Dell David Monahan
Publisher :
ISBN 13 :
Total Pages : 236 pages
Book Rating : 4.3/5 (129 download)
Download or read book System Identification by Stochastic Approximation written by Dell David Monahan and published by . This book was released on 1970 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt:
