Simulating Fuzzy Systems PDF Download

Author: James J. Buckley
Publisher: Springer Science & Business Media
ISBN: 9783540241164
Category : Computers
Languages : en
Pages : 236

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Book Description
Simulating Fuzzy Systems demonstrates how many systems naturally become fuzzy systems and shows how regular (crisp) simulation can be used to estimate the alpha-cuts of the fuzzy numbers used to analyze the behavior of the fuzzy system. This monograph presents a concise introduction to fuzzy sets, fuzzy logic, fuzzy estimation, fuzzy probabilities, fuzzy systems theory, and fuzzy computation. It also presents a wide selection of simulation applications ranging from emergency rooms to machine shops to project scheduling, showing the varieties of fuzzy systems.

Author: James J. Buckley
Publisher: Springer
ISBN: 3540312277
Category : Technology & Engineering
Languages : en
Pages : 202

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Book Description
1. 1 Introduction This book is written in two major parts. The ?rst part includes the int- ductory chapters consisting of Chapters 1 through 6. In part two, Chapters 7-26, we present the applications. This book continues our research into simulating fuzzy systems. We started with investigating simulating discrete event fuzzy systems ([7],[13],[14]). These systems can usually be described as queuing networks. Items (transactions) arrive at various points in the s- tem and go into a queue waiting for service. The service stations, preceded by a queue, are connected forming a network of queues and service, until the transaction ?nally exits the system. Examples considered included - chine shops, emergency rooms, project networks, bus routes, etc. Analysis of all of these systems depends on parameters like arrival rates and service rates. These parameters are usually estimated from historical data. These estimators are generally point estimators. The point estimators are put into the model to compute system descriptors like mean time an item spends in the system, or the expected number of transactions leaving the system per unit time. We argued that these point estimators contain uncertainty not shown in the calculations. Our estimators of these parameters become fuzzy numbers, constructed by placing a set of con?dence intervals one on top of another. Using fuzzy number parameters in the model makes it into a fuzzy system. The system descriptors we want (time in system, number leaving per unit time) will be fuzzy numbers.

Author: A. Taylor
Publisher: Createspace Independent Publishing Platform
ISBN: 9781979690508
Category :
Languages : en
Pages : 288

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Book Description
Fuzzy Logic Toolbox provides MATLAB functions, apps, and a Simulink block for analyzing, designing, and simulating systems based on fuzzy logic. The book guides you through the steps of designing fuzzy inference systems. Functions are provided formany common methods, including fuzzy clustering and adaptive neuro fuzzy learning.The toolbox lets you model complex system behaviors using simple logic rules, and then implement these rules in a fuzzy inference system. You can use it as a stand-alone fuzzy inference engine. Alternatively, you can use fuzzy inference blocks in Simulink and simulate the fuzzy systems within a comprehensive model of the entire dynamic system. The most important features that this Toolbox provides are the following: - Fuzzy Logic Design app for building fuzzy inference systems and viewing andanalyzing results - Membership functions for creating fuzzy inference systems - Support for AND, OR, and NOT logic in user-defined rules - Standard Mamdani and Sugeno-type fuzzy inference systems - Automated membership function shaping through neuroadaptive and fuzzy clusteringlearning techniques - Ability to embed a fuzzy inference system in a Simulink model - Ability to generate embeddable C code or stand-alone executable fuzzy inferenceengines

Author: Ching Tai Lin
Publisher: Prentice Hall
ISBN:
Category : Computers
Languages : en
Pages : 824

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Book Description
Neural Fuzzy Systems provides a comprehensive, up-to-date introduction to the basic theories of fuzzy systems and neural networks, as well as an exploration of how these two fields can be integrated to create Neural-Fuzzy Systems. It includes Matlab software, with a Neural Network Toolkit, and a Fuzzy System Toolkit.

Author: S.N. Sivanandam
Publisher: Springer Science & Business Media
ISBN: 3540357815
Category : Technology & Engineering
Languages : en
Pages : 442

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Book Description
This book provides a broad-ranging, but detailed overview of the basics of Fuzzy Logic. The fundamentals of Fuzzy Logic are discussed in detail, and illustrated with various solved examples. The book also deals with applications of Fuzzy Logic, to help readers more fully understand the concepts involved. Solutions to the problems are programmed using MATLAB 6.0, with simulated results. The MATLAB Fuzzy Logic toolbox is provided for easy reference.

Author: Leticia Cervantes
Publisher: Springer
ISBN: 3319266713
Category : Technology & Engineering
Languages : en
Pages : 69

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Book Description
This book focuses on the fields of fuzzy logic, granular computing and also considering the control area. These areas can work together to solve various control problems, the idea is that this combination of areas would enable even more complex problem solving and better results. In this book we test the proposed method using two benchmark problems: the total flight control and the problem of water level control for a 3 tank system. When fuzzy logic is used it make it easy to performed the simulations, these fuzzy systems help to model the behavior of a real systems, using the fuzzy systems fuzzy rules are generated and with this can generate the behavior of any variable depending on the inputs and linguistic value. For this reason this work considers the proposed architecture using fuzzy systems and with this improve the behavior of the complex control problems.

Author: Magdi S. Mahmoud
Publisher: Springer
ISBN: 3319549545
Category : Technology & Engineering
Languages : en
Pages : 689

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Book Description
This textbook explains the principles of fuzzy systems in some depth together with information useful in realizing them within computational processes. The various algorithms and example problem solutions are a well-balanced and pertinent aid for research projects, laboratory work and graduate study. In addition to its worked examples, the book also uses end-of-chapter exercises as an instructional aid with a downloadable solutions manual available to instructors. The content of the book is developed and extended from material taught for four years in the author’s classes. The text provides a broad overview of fuzzy control, estimation and fault diagnosis. It ranges over various classes of target system and modes of control and then turns to filtering, stabilization, and fault detection and diagnosis. Applications, simulation tools and an appendix on algebraic inequalities complete a unified approach to the analysis of single and interconnected fuzzy systems. Fuzzy Control, Estimation and Fault Detection is a guide for final-year undergraduate and graduate students of electrical and mechanical engineering, computer science and information technology, and will also be instructive for professionals in the information technology sector.

Author: Godfrey H.
Publisher: Createspace Independent Publishing Platform
ISBN: 9781540356710
Category :
Languages : en
Pages : 328

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Book Description
Fuzzy Logic Toolbox provides MATLAB functions, graphical tools, and a SimulinkR block for analyzing, designing, and simulating systems based on fuzzy logic. The product guides you through the steps of designing fuzzy inference systems. Functions are provided for many common methods, including fuzzy clustering and adaptive neurofuzzy learning. The toolbox lets you model complex system behaviors using simple logic rules, and then implement these rules in a fuzzy inference system. You can use it as a stand-alone fuzzy inference engine. Alternatively, you can use fuzzy inference blocks in Simulink and simulate the fuzzy systems within a comprehensive model of the entire dynamic system. The more important features are the next:* Specialized GUIs for building fuzzy inference systems and viewing and analyzing results* Membership functions for creating fuzzy inference systems * Support for AND, OR, and NOT logic in user-defined rules* Standard Mamdani and Sugeno-type fuzzy inference systems* Automated membership function shaping through neuroadaptive and fuzzy clustering learning techniques* Ability to embed a fuzzy inference system in a Simulink model * Ability to generate embeddable C code or stand-alone executable fuzzy inference engines

Author: S.N. Sivanandam
Publisher: Springer Science & Business Media
ISBN: 3540731903
Category : Technology & Engineering
Languages : en
Pages : 442

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Book Description
This book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. In addition, the book presents implementation of optimization problems using C and C++ as well as simulated solutions for genetic algorithm problems using MATLAB 7.0. It also includes application case studies on genetic algorithms in emerging fields.

Author: Gang Feng
Publisher: CRC Press
ISBN: 1420092650
Category : Technology & Engineering
Languages : en
Pages : 299

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Book Description
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®.