Fuzzy Logic

Multi-Sensor Data Fusion with MATLAB

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Title:
Multi-Sensor Data Fusion with MATLAB
Authors:
Jitendra R. Raol
Edition:
2009
Publisher:
CRC Press
Pages:
568
Language:
English
ISBN-10
1439800030
ISBN-13
978-1439800034
Format:
PDF + MATLAB programs (ZIP)
Size (MB):
8

Book Description:
Fusing sensors’ data can lead to numerous benefits in a system’s performance. Through real-world examples and the evaluation of algorithmic results, this detailed book provides an understanding of MSDF concepts and methods from a practical point of view.

Table of Contents:
Theory of Data Fusion and Kinematic-Level Fusion, J.R. Raol, G. Girija, and N. Shanthakumar
Introduction
Concepts and Theory of Data Fusion
Strategies and Algorithms for Target Tracking and Data Fusion
Performance Evaluation of Data Fusion Systems, Software, and Tracking

Fuzzy Logic and Decision Fusion, J.R. Raol and S.K. Kashyap
Introduction
Theory of Fuzzy Logic
Decision Fusion
Performance Evaluation of Fuzzy Logic-Based Decision Systems

Pixel and Feature-Level Image Fusion, J.R. Raol and V.P.S. Naidu
Introduction
Pixel- and Feature-Level Image Fusion Concepts and Algorithms
Performance Evaluation of Image-Based Data Fusion Systems

A Brief on Data Fusion in Other Systems, Ajith Gopal and Simukai Utete
Introduction: Overview of Data Fusion in Mobile Intelligent Autonomous Systems
Intelligent Monitoring and Fusion

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Fuzzy Systems Engineering: Theory and Practice

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Book Description
This book is devoted to reporting innovative and significant progress in fuzzy system engineering. Given the maturation of fuzzy logic, this book is dedicated to exploring the recent breakthroughs in fuzziness and soft computing in favor of intelligent system engineering. This monograph presents novel developments of the fuzzy theory as well as interesting applications of the fuzzy logic exploiting the theory to engineer intelligent systems.

Table of Contents
Part I Fuzzy Theory.
Introducing You to Fuzziness.
A Qualitative Approach for Symbolic Data Manipulation Under Uncertainty.
Adaptation of Fuzzy Inference System Using Neural Learning.
Part II Fuzzy Systems.
A fuzzy approach on guiding model for interception flight.
On the Stability and Sensitivity Analysis of Fuzzy Control Systems for Servo-systems.
Applications of Fuzzy Logic in Mobile Robots Control.
Modeling the Tennessee Eastman Chemical Process Reactor Using Fuzzy Logic.

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Fuzzy Logic for Embedded Systems Applications

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Book Description
Fuzzy Logic for Embedded Systems Applications, by a recognized expert in the field, covers all the basic theory relevant to electronics design, with particular emphasis on embedded systems, and shows how the techniques can be applied to shorten design cycles and handle logic problems that are tough to solve using conventional linear techniques. All the latest advances in the field aree discussed and practical circuit design examples presented.

Fuzzy logic has been found to be particularly suitable for many embedded control applications. The intuitive nature of the fuzzy-based system design saves engineers time and reduces costs by shortening product development cycles and making system maintenance and adjustments easier. Yet despite its wide acceptance-and perhaps because of its name-it is still misunderstood and feared by many engineers. There is a need for embedded systems designers-both hardware and software-to get up to speed on the principles and applications of fuzzy logic in order to ascertain when and how to use them appropriately.

Fuzzy Logic for Embedded Systems Applications provides practical guidelines for designing electronic circuits and devices for embedded systems using fuzzy-based logic. It covers both theory and applications with design examples.

* Unified approach to fuzzy electronics from an engineering point of view
* Easy to follow with plenty of examples
* Review and evaluation of free resources

Table of Contents
1. Embedded Systems: An Overview
2. Fuzzy Sets
3. Fuzzy Relations
4. Embedded Fuzzy Applications
5. Critique of Fuzzy Logic
6. Neural Networks
7. Hybrid Systems
8. Hardware Implementations
9. Software Tools

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Fuzzy Control and Identification

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Book Description
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.

Table of Contents
CHAPTER 1 INTRODUCTION.
CHAPTER 2 BASIC CONCEPTS OF FUZZY SETS.
CHAPTER 3 MAMDANI FUZZY SYSTEMS.
CHAPTER 4 FUZZY CONTROL WITH MAMDANI SYSTEMS.
CHAPTER 5 MODELING AND CONTROL METHODS USEFUL FOR FUZZY CONTROL.
CHAPTER 6 TAKAGI–SUGENO FUZZY SYSTEMS.
CHAPTER 7 PARALLEL DISTRIBUTED CONTROL WITH TAKAGI–SUGENO FUZZY SYSTEMS.
CHAPTER 8 ESTIMATION OF STATIC NONLINEAR FUNCTIONS FROM DATA.
CHAPTER 9 MODELING OF DYNAMIC PLANTS AS FUZZY SYSTEMS.
CHAPTER 10 ADAPTIVE FUZZY CONTROL.

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