Syllabus

1. Introduction
   • Objective
   • Overview
2. Fuzzy Set Theory
   • Fuzzy sets
     • Fuzzy sets and classic fuzzy operators
     • MF formulation and parameterization
     • Extended fuzzy union, intersection, and complement
   • Fuzzy rules and fuzzy reasoning
     • Extension principle
     • Fuzzy relations
     • Fuzzy if-then rules
     • Fuzzy reasoning
   • Fuzzy inference systems
     • Mamdani's fuzzy models
     • Sugeno's fuzzy models
     • Tsukamoto's fuzzy models
     • Other variants
   • Others
     • Fuzzy arithmetic
     • Fuzzy clustering
3. Regression and optimization
   • Least-squares estimator
     • Matrix techniques
     • Least-squares estimator and its geometric interpretation
     • Recursive Least-squares estimator
     • Recursive Least-squares estimator with forgetting factors
     • Maximum likelihood estimator
   • Gradient-based optimization
     • Steepest descent
     • Newton's method
     • Step size determination
     • Gauss-Newton method
     • Levenberg-Margquardt method
   • Gradient-free optimization
     • Genetic algorithms
     • Simulated annealing
     • Downhill search
     • Random search
4. Adaptive fuzzy inference systems
   • Adaptive networks
     • Architectures
     • Learning rules
   • Adaptive neuro-fuzzy inference systems (ANFIS)
     • Architectures
     • Hybrid learning rules
5. Applications
   • Data modeling
   • Pattern recognition
   • Adaptive fuzzy control