Fuzzy Rule-Based Systems

The following sections are included:

• Framework: Fuzzy Logic and Fuzzy Systems

• Mamdani Fuzzy Rule-Based Systems

  • The knowledge base of Mamdani fuzzy rule-based systems

  • The inference engine of Mamdani fuzzy rule-based systems

    • The fuzzification interface

    • The inference system

    • The defuzzification interface

  • Example of application

  • Design of the inference engine

  • Advantages and drawbacks of Mamdani-type fuzzy rule-based systems

  • Variants of Mamdani fuzzy rule-based systems

    • DNF Mamdani fuzzy rule-based systems

    • Approximate Mamdani-type fuzzy rule-based systems

• Takagi–Sugeno–Kang Fuzzy Rule-Based Systems

• Generation of the Fuzzy Rule Set

  • Design tasks for obtaining the fuzzy rule set

  • Kinds of information available to define the fuzzy rule set

  • Generation of linguistic rules

  • Generation of approximate Mamdani-type fuzzy rules

  • Generation of TSK fuzzy rules

  • Basic properties of fuzzy rule sets

    • Completeness of a fuzzy rule set

    • Consistency of a fuzzy rule set

    • Low complexity of a fuzzy rule set

    • Redundancy of a fuzzy rule set

• Applying Fuzzy Rule-Based Systems

  • Fuzzy modelling

    • Benefits of using fuzzy rule-based systems for modelling

    • Relationship between fuzzy modelling and system identification

    • Some applications of fuzzy modelling

  • Fuzzy control

    • Advantages of fuzzy logic controllers

    • Differences between the design of fuzzy logic controllers and fuzzy models

    • Some applications of fuzzy control

  • Fuzzy classification

    • Advantages of using fuzzy rule-based systems for classification

    • Components and design of fuzzy rule-based classification systems

    • Some applications of fuzzy classification