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Author: David Leonardo Arengas Rojas Publisher: BoD – Books on Demand ISBN: 3737610096 Category : Technology & Engineering Languages : en Pages : 182
Book Description
Performing experiments for system identification of continuously operated plants might be restricted as it can impact negatively normal production. In such cases, using historical logged data can become an attractive alternative for system identification. However, operating points are rarely changed and parameter estimation methods can suffer numerical problems. Three main drawbacks of current approaches in this research area can be discussed. Firstly, detection tests are not adapted for dynamical systems. Secondly, methods to define upper interval bounds are not robust to colored noise that is more likely to be found in real applications. Thirdly, model estimation with the retrieved data is not supported and the performance of the method cannot be assessed. In the method proposed in this work, called data selection for system identification (DS4SID), previous drawbacks are addressed and robust tests are designed and implemented. The performance of DS4SID is evaluated in a simulated and laboratory multivariate processes. A process unit of the lab-scale factory “μPlant” is used as industryoriented case study. Models estimated with selected data are shown to have similar performance than estimates with the entire data set.
Author: David Leonardo Arengas Rojas Publisher: BoD – Books on Demand ISBN: 3737610096 Category : Technology & Engineering Languages : en Pages : 182
Book Description
Performing experiments for system identification of continuously operated plants might be restricted as it can impact negatively normal production. In such cases, using historical logged data can become an attractive alternative for system identification. However, operating points are rarely changed and parameter estimation methods can suffer numerical problems. Three main drawbacks of current approaches in this research area can be discussed. Firstly, detection tests are not adapted for dynamical systems. Secondly, methods to define upper interval bounds are not robust to colored noise that is more likely to be found in real applications. Thirdly, model estimation with the retrieved data is not supported and the performance of the method cannot be assessed. In the method proposed in this work, called data selection for system identification (DS4SID), previous drawbacks are addressed and robust tests are designed and implemented. The performance of DS4SID is evaluated in a simulated and laboratory multivariate processes. A process unit of the lab-scale factory “μPlant” is used as industryoriented case study. Models estimated with selected data are shown to have similar performance than estimates with the entire data set.
Author: Rolf Isermann Publisher: Springer ISBN: 9783540871552 Category : Technology & Engineering Languages : en Pages : 705
Book Description
Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods that provide models during the measurement. The book is theory-oriented and application-oriented and most methods covered have been used successfully in practical applications for many different processes. Illustrative examples in this book with real measured data range from hydraulic and electric actuators up to combustion engines. Real experimental data is also provided on the Springer webpage, allowing readers to gather their first experience with the methods presented in this book. Among others, the book covers the following subjects: determination of the non-parametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation with a focus on the method of Least Squares and modifications, identification of time-variant processes, identification in closed-loop, identification of continuous time processes, and subspace methods. Some methods for nonlinear system identification are also considered, such as the Extended Kalman filter and neural networks. The different methods are compared by using a real three-mass oscillator process, a model of a drive train. For many identification methods, hints for the practical implementation and application are provided. The book is intended to meet the needs of students and practicing engineers working in research and development, design and manufacturing.
Author: Karel J. Keesman Publisher: Springer Science & Business Media ISBN: 0857295225 Category : Technology & Engineering Languages : en Pages : 334
Book Description
System Identification shows the student reader how to approach the system identification problem in a systematic fashion. The process is divided into three basic steps: experimental design and data collection; model structure selection and parameter estimation; and model validation, each of which is the subject of one or more parts of the text. Following an introduction on system theory, particularly in relation to model representation and model properties, the book contains four parts covering: • data-based identification – non-parametric methods for use when prior system knowledge is very limited; • time-invariant identification for systems with constant parameters; • time-varying systems identification, primarily with recursive estimation techniques; and • model validation methods. A fifth part, composed of appendices, covers the various aspects of the underlying mathematics needed to begin using the text. The book uses essentially semi-physical or gray-box modeling methods although data-based, transfer-function system descriptions are also introduced. The approach is problem-based rather than rigorously mathematical. The use of finite input–output data is demonstrated for frequency- and time-domain identification in static, dynamic, linear, nonlinear, time-invariant and time-varying systems. Simple examples are used to show readers how to perform and emulate the identification steps involved in various control design methods with more complex illustrations derived from real physical, chemical and biological applications being used to demonstrate the practical applicability of the methods described. End-of-chapter exercises (for which a downloadable instructors’ Solutions Manual is available from fill in URL here) will both help students to assimilate what they have learned and make the book suitable for self-tuition by practitioners looking to brush up on modern techniques. Graduate and final-year undergraduate students will find this text to be a practical and realistic course in system identification that can be used for assessing the processes of a variety of engineering disciplines. System Identification will help academic instructors teaching control-related to give their students a good understanding of identification methods that can be used in the real world without the encumbrance of undue mathematical detail.
Author: Christopher D. Manning Publisher: Cambridge University Press ISBN: 1139472100 Category : Computers Languages : en Pages :
Book Description
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
Author: Kenneth Train Publisher: Cambridge University Press ISBN: 0521766559 Category : Business & Economics Languages : en Pages : 399
Book Description
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Author: Pang-Ning Tan Publisher: Pearson Education India ISBN: 9332586055 Category : Languages : en Pages : 780
Book Description
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. Each major topic is organized into two chapters, beginni
Author: Mihai Christodorescu Publisher: Springer Science & Business Media ISBN: 0387445994 Category : Computers Languages : en Pages : 307
Book Description
This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. The book analyzes current trends in malware activity online, including botnets and malicious code for profit, and it proposes effective models for detection and prevention of attacks using. Furthermore, the book introduces novel techniques for creating services that protect their own integrity and safety, plus the data they manage.
Author: David J. C. MacKay Publisher: Cambridge University Press ISBN: 9780521642989 Category : Computers Languages : en Pages : 694
Book Description
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
Author: Wang, John Publisher: IGI Global ISBN: 1591405599 Category : Computers Languages : en Pages : 1382
Book Description
Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead. The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining (DWM). This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of DWM in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of DWM.
Author: David Leonardo Arengas Rojas
Author: David Leonardo Arengas Rojas
Author: Rolf Isermann
Author: 
Author: Karel J. Keesman
Author: Christopher D. Manning
Author: Kenneth Train
Author: Pang-Ning Tan
Author: Mihai Christodorescu
Author: David J. C. MacKay
Author: Wang, John