Fuzzy Model Identification
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Author |
: Hans Hellendoorn |
Publisher |
: Springer |
Total Pages |
: 350 |
Release |
: 1997 |
ISBN-10 |
: UOM:39015040535869 |
ISBN-13 |
: |
Rating |
: 4/5 (69 Downloads) |
Synopsis Fuzzy Model Identification by : Hans Hellendoorn
Introduction; General overview; Fuzzy identification from a grey box modeling point of view; Clustering methods; Constructing fuzzy models by product space clustering; Identification of Takagi-Sugeno fuzzy models via clustering and Hough transform; Rapid prototyping of fuzzy models based on hierarchical clustering; Neural networks; Fuzzy identification using methods of intelligent data analysis; Identification of singleton fuzzy models via fuzzy hyperrectangular composite NN; Genetic algorithms; Identification of linguistic fuzzy models by means of genetic algorithms.; Optimization of fuzzy models by global numeric optimizaton; Artificial intelligence; Identification of linguistic fuzzy models based on learning.
Author |
: Hans Hellendoorn |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 334 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9783642607677 |
ISBN-13 |
: 3642607675 |
Rating |
: 4/5 (77 Downloads) |
Synopsis Fuzzy Model Identification by : Hans Hellendoorn
During the past few years two principally different approaches to the design of fuzzy controllers have emerged: heuristics-based design and model-based design. The main motivation for the heuristics-based design is given by the fact that many industrial processes are still controlled in one of the following two ways: - The process is controlled manually by an experienced operator. - The process is controlled by an automatic control system which needs manual, on-line 'trimming' of its parameters by an experienced operator. In both cases it is enough to translate in terms of a set of fuzzy if-then rules the operator's manual control algorithm or manual on-line 'trimming' strategy in order to obtain an equally good, or even better, wholly automatic fuzzy control system. This implies that the design of a fuzzy controller can only be done after a manual control algorithm or trimming strategy exists. It is admitted in the literature on fuzzy control that the heuristics-based approach to the design of fuzzy controllers is very difficult to apply to multiple-inputjmultiple-output control problems which represent the largest part of challenging industrial process control applications. Furthermore, the heuristics-based design lacks systematic and formally verifiable tuning tech niques. Also, studies of the stability, performance, and robustness of a closed loop system incorporating a heuristics-based fuzzy controller can only be done via extensive simulations.
Author |
: Janos Abonyi |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 279 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461200277 |
ISBN-13 |
: 146120027X |
Rating |
: 4/5 (77 Downloads) |
Synopsis Fuzzy Model Identification for Control by : Janos Abonyi
This book presents new approaches to constructing fuzzy models for model-based control. Simulated examples and real-world applications from chemical and process engineering illustrate the main methods and techniques. Supporting MATLAB and Simulink files create a computational platform for exploration of the concepts and algorithms.
Author |
: John H. Lilly |
Publisher |
: John Wiley & Sons |
Total Pages |
: 199 |
Release |
: 2011-03-10 |
ISBN-10 |
: 9781118097816 |
ISBN-13 |
: 1118097815 |
Rating |
: 4/5 (16 Downloads) |
Synopsis Fuzzy Control and Identification by : John H. Lilly
This book gives an introduction to basic fuzzy logic and Mamdani and Takagi-Sugeno fuzzy systems. The text shows how these can be used to control complex nonlinear engineering systems, while also also suggesting several approaches to modeling of complex engineering systems with unknown models. Finally, fuzzy modeling and control methods are combined in the book, to create adaptive fuzzy controllers, ending with an example of an obstacle-avoidance controller for an autonomous vehicle using modus ponendo tollens logic.
Author |
: Jairo Jose Espinosa Oviedo |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 274 |
Release |
: 2007-01-04 |
ISBN-10 |
: 9781846280870 |
ISBN-13 |
: 1846280877 |
Rating |
: 4/5 (70 Downloads) |
Synopsis Fuzzy Logic, Identification and Predictive Control by : Jairo Jose Espinosa Oviedo
Modern industrial processes and systems require adaptable advanced control protocols able to deal with circumstances demanding "judgement” rather than simple "yes/no”, "on/off” responses: circumstances where a linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious for this purpose. Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real industrial systems and simulations. The second part exploits such models to design control systems employing techniques like data mining. This monograph presents a combination of fuzzy control theory and industrial serviceability that will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.
Author |
: Robert Babuška |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 269 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9789401148689 |
ISBN-13 |
: 9401148686 |
Rating |
: 4/5 (89 Downloads) |
Synopsis Fuzzy Modeling for Control by : Robert Babuška
Rule-based fuzzy modeling has been recognised as a powerful technique for the modeling of partly-known nonlinear systems. Fuzzy models can effectively integrate information from different sources, such as physical laws, empirical models, measurements and heuristics. Application areas of fuzzy models include prediction, decision support, system analysis, control design, etc. Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering points of view. It focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements (fuzzy identification), and on the design of nonlinear controllers based on fuzzy models. To automatically generate fuzzy models from measurements, a comprehensive methodology is developed which employs fuzzy clustering techniques to partition the available data into subsets characterized by locally linear behaviour. The relationships between the presented identification method and linear regression are exploited, allowing for the combination of fuzzy logic techniques with standard system identification tools. Attention is paid to the trade-off between the accuracy and transparency of the obtained fuzzy models. Control design based on a fuzzy model of a nonlinear dynamic process is addressed, using the concepts of model-based predictive control and internal model control with an inverted fuzzy model. To this end, methods to exactly invert specific types of fuzzy models are presented. In the context of predictive control, branch-and-bound optimization is applied. The main features of the presented techniques are illustrated by means of simple examples. In addition, three real-world applications are described. Finally, software tools for building fuzzy models from measurements are available from the author.
Author |
: Witold Pedrycz |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 399 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461313656 |
ISBN-13 |
: 1461313651 |
Rating |
: 4/5 (56 Downloads) |
Synopsis Fuzzy Modelling by : Witold Pedrycz
Fuzzy Modelling: Paradigms and Practice provides an up-to-date and authoritative compendium of fuzzy models, identification algorithms and applications. Chapters in this book have been written by the leading scholars and researchers in their respective subject areas. Several of these chapters include both theoretical material and applications. The editor of this volume has organized and edited the chapters into a coherent and uniform framework. The objective of this book is to provide researchers and practitioners involved in the development of models for complex systems with an understanding of fuzzy modelling, and an appreciation of what makes these models unique. The chapters are organized into three major parts covering relational models, fuzzy neural networks and rule-based models. The material on relational models includes theory along with a large number of implemented case studies, including some on speech recognition, prediction, and ecological systems. The part on fuzzy neural networks covers some fundamentals, such as neurocomputing, fuzzy neurocomputing, etc., identifies the nature of the relationship that exists between fuzzy systems and neural networks, and includes extensive coverage of their architectures. The last part addresses the main design principles governing the development of rule-based models. Fuzzy Modelling: Paradigms and Practice provides a wealth of specific fuzzy modelling paradigms, algorithms and tools used in systems modelling. Also included is a panoply of case studies from various computer, engineering and science disciplines. This should be a primary reference work for researchers and practitioners developing models of complex systems.
Author |
: Robert Babuška |
Publisher |
: |
Total Pages |
: 294 |
Release |
: 1997 |
ISBN-10 |
: 9090101535 |
ISBN-13 |
: 9789090101538 |
Rating |
: 4/5 (35 Downloads) |
Synopsis Fuzzy modeling and identification by : Robert Babuška
Author |
: Gang Feng |
Publisher |
: CRC Press |
Total Pages |
: 299 |
Release |
: 2018-09-03 |
ISBN-10 |
: 9781420092653 |
ISBN-13 |
: 1420092650 |
Rating |
: 4/5 (53 Downloads) |
Synopsis Analysis and Synthesis of Fuzzy Control Systems by : Gang Feng
Fuzzy logic control (FLC) has proven to be a popular control methodology for many complex systems in industry, and is often used with great success as an alternative to conventional control techniques. However, because it is fundamentally model free, conventional FLC suffers from a lack of tools for systematic stability analysis and controller design. To address this problem, many model-based fuzzy control approaches have been developed, with the fuzzy dynamic model or the Takagi and Sugeno (T–S) fuzzy model-based approaches receiving the greatest attention. Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach offers a unique reference devoted to the systematic analysis and synthesis of model-based fuzzy control systems. After giving a brief review of the varieties of FLC, including the T–S fuzzy model-based control, it fully explains the fundamental concepts of fuzzy sets, fuzzy logic, and fuzzy systems. This enables the book to be self-contained and provides a basis for later chapters, which cover: T–S fuzzy modeling and identification via nonlinear models or data Stability analysis of T–S fuzzy systems Stabilization controller synthesis as well as robust H∞ and observer and output feedback controller synthesis Robust controller synthesis of uncertain T–S fuzzy systems Time-delay T–S fuzzy systems Fuzzy model predictive control Robust fuzzy filtering Adaptive control of T–S fuzzy systems A reference for scientists and engineers in systems and control, the book also serves the needs of graduate students exploring fuzzy logic control. It readily demonstrates that conventional control technology and fuzzy logic control can be elegantly combined and further developed so that disadvantages of conventional FLC can be avoided and the horizon of conventional control technology greatly extended. Many chapters feature application simulation examples and practical numerical examples based on MATLAB®.
Author |
: Huaguang Zhang |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 423 |
Release |
: 2007-10-17 |
ISBN-10 |
: 9780817645397 |
ISBN-13 |
: 081764539X |
Rating |
: 4/5 (97 Downloads) |
Synopsis Fuzzy Modeling and Fuzzy Control by : Huaguang Zhang
Fuzzy logic methodology has proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology is applicable to many real-world problems, especially in the area of consumer products. This book presents the first comprehensive, unified treatment of fuzzy modeling and fuzzy control, providing tools for the control of complex nonlinear systems. Coverage includes model complexity, model precision, and computing time. This is an excellent reference for electrical, computer, chemical, industrial, civil, manufacturing, mechanical and aeronautical engineers, and also useful for graduate courses in electrical engineering, computer engineering, and computer science.