Fuzzy logic has virtually exploded over the landscape of emerging technologies, becoming an integral part of myriad applications and a standard tool for engineers. Until recently, most of the attention and applications have centered on fuzzy systems implemented in software. But these systems are limited. Problems that require real-time operation, low area, or low power consumption demand hardware designed to the fuzzy paradigm - and engineers with the background and skills to design it.

    Microelectronic Design of Fuzzy Logic-Based Systems offers low-cost answers to issues that software cannot resolve. From the theoretical, architectural, and technological foundation to design tools and applications, it serves as your guide to effective hardware realizations of fuzzy logic.

  • Review fuzzy logic theory and the basic issues of fuzzy sets, operators, and inference mechanisms
  • Explore the trade-offs between efficient theoretical behavior and practical hardware realizations
  • Discover the properties of the possible microelectronic realizations of fuzzy systems - conventional processors, fuzzy coprocessors, and fuzzy chips
  • Investigate the design of fuzzy chips that implement the whole fuzzy inference method into silicon
  • Analyze analog, digital, and mixed-signal techniques
  • Reduce your design effort for fuzzy systems with CAD tools - learn the requirements they should meet and survey current environments.
  • Put it all together - see examples and case studies illustrating how all of this is used to solve particular problems related to control and neuro-fuzzy applications
  • INTRODUCTION
    Methods for Information Representation and Processing
    Fuzzy Sets and Fuzzy Logic
    A Brief History of Fuzzy Logic
    Fuzzy Logic Application Domain
    FUZZY SET THEORY
    Fuzzy Sets
    The Concept of Membership Function
    Terminology for Fuzzy Sets
    Operations with Fuzzy Sets
    Fuzzy Relations
    FUZZY INFERENCE SYSTEMS
    Linguistic Variables
    Fuzzy Rules
    Approximate Reasoning Techniques
    Rule-Based Inference Mechanisms
    Defuzzification Methods
    Types of Fuzzy Systems
    Structure of a Fuzzy System
    FUZZY SYSTEM DEVELOPMENT
    Definition of a Fuzzy System
    CAD Tools for Fuzzy Systems
    The Xfuzzy Development Environment
    FUZZY SYSTEM VERIFICATION
    Learning Techniques for Fuzzy Systems
    Learning on XFL-Based Systems
    Simulation of Fuzzy Systems
    Simulation of XFL-Based Systems
    On-Line Verification of XFL-Based Systems
    HARDWARE REALIZATION OF FUZZY SYSTEMS
    Fuzzy System Implementations Depending on the Application
    Fuzzy System Realizations with General-Purpose Processors
    Fuzzy System Realizations with Dedicated Hardware
    CONTINUOUS-TIME ANALOG TECHNIQUES FOR DESIGNING FUZZY INTEGRATED CIRCUITS
    The First Analog Fuzzy Integrated Circuits
    Fully Parallel Architectures
    Fuzzification Stage
    Rule Processing Stage
    Output Stage
    Design of a Current-Mode CMOS Prototype
    DISCRETE-TIME ANALOG TECHNIQUES FOR DESIGNING FUZZY INTEGRATED CIRCUITS
    Sequential Architectures
    Fuzzification Stage
    Rule Processing Stage
    Output Stage
    Timing and Inference Speed of Discrete-Time Singleton Fuzzy ICs
    Design of an SC CMOS Prototype
    DIGITAL TECHNIQUES FOR DESIGNING FUZZY INTEGRATED CIRCUITS
    The First Digital Fuzzy Integrated Circuits
    Architectures of Digital Fuzzy IntegratedCircuits
    Fuzzification Stage
    Rule Processing and Defuzzification Stages
    Active Rule-Driven Architecture with Simplified Defuzzification Methods
    FUZZY SYSTEM SYNTHESIS
    Hardware Synthesis of Fuzzy Systems
    Describing Fuzzy Systems with a Hardware Description Language: VHDL
    Tools for Hardware Synthesis in Xfuzzy
    A Design Methodology for Fuzzy Systems
    FUZZY SYSTEMS AS CONTROLLERS
    Conventional Control Systems
    Fuzzy Control Systems
    Application Example: Ball Suspended by an Air Flow
    FUZZY SYSTEMS AS APPROXIMATORS
    Approximation Capability of Fuzzy Systems
    Applications using Off-Chip Learning
    Application using On-Chip Learning

    Biography

    Iluminada Baturone, Angel Barriga, Carlos Jimenez-Fernandez, Diego R. Lopez, Santiago Sanchez-Solano