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System Identification of Stochastic Nonlinear Dynamic Systems using Takagi-Sugeno Fuzzy Models

System Identification of Stochastic Nonlinear Dynamic Systems using Takagi-Sugeno Fuzzy Models
Author: Salman Zaidi
Publisher: kassel university press GmbH
Total Pages: 155
Release: 2019-02-22
Genre: Fuzzy systems
ISBN: 3737606501
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Some novel approaches to estimate Nonlinear Output Error (NOE) models using TS fuzzy models for a class of nonlinear dynamic systems having variability in their outputs is presented in this dissertation. Instead of using unrealistic assumptions about uncertainty, the most common of which is normality, the proposed methodology tends to capture effects caused by the real uncertainty observed in the data. The methodology requires that the identification method must be repeated offline a number of times under similar conditions. This leads to multiple inputoutput time series from the underlying system. These time series are preprocessed using the techniques of statistics and probability theory to generate the envelopes of response at each time instant. By incorporating interval data in fuzzy modelling and using the theory of symbolic interval-valued data, a TS fuzzy model with interval antecedent and consequent parameters is obtained. The proposed identification algorithm provides for a model for predicting the center-valued response as well as envelopes as the measure of uncertainty in system output.

On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification

On the Detection and Selection of Informative Subsequences from Large Historical Data Records for Linear System Identification
Author: David Leonardo Arengas Rojas
Publisher: BoD – Books on Demand
Total Pages: 182
Release: 2022-01-01
Genre: Technology & Engineering
ISBN: 3737610096
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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.

Stochastic Control

Stochastic Control
Author: Chris Myers
Publisher: BoD – Books on Demand
Total Pages: 663
Release: 2010-08-17
Genre: Computers
ISBN: 9533071214
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Uncertainty presents significant challenges in the reasoning about and controlling of complex dynamical systems. To address this challenge, numerous researchers are developing improved methods for stochastic analysis. This book presents a diverse collection of some of the latest research in this important area. In particular, this book gives an overview of some of the theoretical methods and tools for stochastic analysis, and it presents the applications of these methods to problems in systems theory, science, and economics.

Nonlinear System Identification

Nonlinear System Identification
Author: Oliver Nelles
Publisher: Springer Science & Business Media
Total Pages: 785
Release: 2013-03-09
Genre: Technology & Engineering
ISBN: 3662043238
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Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.

Evolving Intelligent Systems

Evolving Intelligent Systems
Author: Plamen Angelov
Publisher: John Wiley & Sons
Total Pages: 464
Release: 2010-03-25
Genre: Computers
ISBN: 9780470569955
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From theory to techniques, the first all-in-one resource for EIS There is a clear demand in advanced process industries, defense, and Internet and communication (VoIP) applications for intelligent yet adaptive/evolving systems. Evolving Intelligent Systems is the first self- contained volume that covers this newly established concept in its entirety, from a systematic methodology to case studies to industrial applications. Featuring chapters written by leading world experts, it addresses the progress, trends, and major achievements in this emerging research field, with a strong emphasis on the balance between novel theoretical results and solutions and practical real-life applications. Explains the following fundamental approaches for developing evolving intelligent systems (EIS): the Hierarchical Prioritized Structure the Participatory Learning Paradigm the Evolving Takagi-Sugeno fuzzy systems (eTS+) the evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithm Emphasizes the importance and increased interest in online processing of data streams Outlines the general strategy of using the fuzzy dynamic clustering as a foundation for evolvable information granulation Presents a methodology for developing robust and interpretable evolving fuzzy rule-based systems Introduces an integrated approach to incremental (real-time) feature extraction and classification Proposes a study on the stability of evolving neuro-fuzzy recurrent networks Details methodologies for evolving clustering and classification Reveals different applications of EIS to address real problems in areas of: evolving inferential sensors in chemical and petrochemical industry learning and recognition in robotics Features downloadable software resources Evolving Intelligent Systems is the one-stop reference guide for both theoretical and practical issues for computer scientists, engineers, researchers, applied mathematicians, machine learning and data mining experts, graduate students, and professionals.

Advances in Neural Networks - ISNN 2007

Advances in Neural Networks - ISNN 2007
Author: Derong Liu
Publisher: Springer
Total Pages: 1390
Release: 2007-07-14
Genre: Computers
ISBN: 3540723838
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This book is part of a three volume set that constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. Coverage includes neural networks for control applications, robotics, data mining and feature extraction, chaos and synchronization, support vector machines, fault diagnosis/detection, image/video processing, and applications of neural networks.

Stochastic Distribution Control System Design

Stochastic Distribution Control System Design
Author: Lei Guo
Publisher: Springer Science & Business Media
Total Pages: 201
Release: 2010-05-13
Genre: Technology & Engineering
ISBN: 1849960305
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A recent development in SDC-related problems is the establishment of intelligent SDC models and the intensive use of LMI-based convex optimization methods. Within this theoretical framework, control parameter determination can be designed and stability and robustness of closed-loop systems can be analyzed. This book describes the new framework of SDC system design and provides a comprehensive description of the modelling of controller design tools and their real-time implementation. It starts with a review of current research on SDC and moves on to some basic techniques for modelling and controller design of SDC systems. This is followed by a description of controller design for fixed-control-structure SDC systems, PDF control for general input- and output-represented systems, filtering designs, and fault detection and diagnosis (FDD) for SDC systems. Many new LMI techniques being developed for SDC systems are shown to have independent theoretical significance for robust control and FDD problems.

System Identification (SYSID '03)

System Identification (SYSID '03)
Author: Paul Van Den Hof
Publisher: Elsevier
Total Pages: 2080
Release: 2004-06-29
Genre: Science
ISBN: 9780080437095
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The scope of the symposium covers all major aspects of system identification, experimental modelling, signal processing and adaptive control, ranging from theoretical, methodological and scientific developments to a large variety of (engineering) application areas. It is the intention of the organizers to promote SYSID 2003 as a meeting place where scientists and engineers from several research communities can meet to discuss issues related to these areas. Relevant topics for the symposium program include: Identification of linear and multivariable systems, identification of nonlinear systems, including neural networks, identification of hybrid and distributed systems, Identification for control, experimental modelling in process control, vibration and modal analysis, model validation, monitoring and fault detection, signal processing and communication, parameter estimation and inverse modelling, statistical analysis and uncertainty bounding, adaptive control and data-based controller tuning, learning, data mining and Bayesian approaches, sequential Monte Carlo methods, including particle filtering, applications in process control systems, motion control systems, robotics, aerospace systems, bioengineering and medical systems, physical measurement systems, automotive systems, econometrics, transportation and communication systems *Provides the latest research on System Identification *Contains contributions written by experts in the field *Part of the IFAC Proceedings Series which provides a comprehensive overview of the major topics in control engineering.

Nonlinear System Identification

Nonlinear System Identification
Author: Oliver Nelles
Publisher: Springer Nature
Total Pages: 1235
Release: 2020-09-09
Genre: Science
ISBN: 3030474399
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This book provides engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. It equips them to apply the models and methods discussed to real problems with confidence, while also making them aware of potential difficulties that may arise in practice. Moreover, the book is self-contained, requiring only a basic grasp of matrix algebra, signals and systems, and statistics. Accordingly, it can also serve as an introduction to linear system identification, and provides a practical overview of the major optimization methods used in engineering. The focus is on gaining an intuitive understanding of the subject and the practical application of the techniques discussed. The book is not written in a theorem/proof style; instead, the mathematics is kept to a minimum, and the ideas covered are illustrated with numerous figures, examples, and real-world applications. In the past, nonlinear system identification was a field characterized by a variety of ad-hoc approaches, each applicable only to a very limited class of systems. With the advent of neural networks, fuzzy models, Gaussian process models, and modern structure optimization techniques, a much broader class of systems can now be handled. Although one major aspect of nonlinear systems is that virtually every one is unique, tools have since been developed that allow each approach to be applied to a wide variety of systems.