Genetic Adapt Fuzzy Control System Why Fuzzy The real world is not black and white. The da

                   Genetic Adapt Fuzzy Control System

Why Fuzzy
      The real world is not black and white.  The day in and day out human
      reasoning is all but EXACT.  However, the classical logic and the
      traditional control systems are based on exact reasoning.  Fuzzy logic,
      also known as imprecise reasoning, allows us solving problems with fuzzy
      boundaries as we handle daily routines.  Fuzzy logic is a formulation of
      logic using mathematical framework to deal concepts such as few, very,
      much, and many.  Fuzzy logic can help us make decisions when lack of
      specific facts or the knowledge is imprecise or far from complete.

      Human experts can handle situations beyond their knowledge with
      gracefully degraded accuracy, but the traditional control systems
      normally breakdown when the environment is out of their design scope. 
      Many environments are difficult to model mathematically, especially for
      systems with uncertainties or non-linearties.  When applying
      conventional control logic to these environments, it requires human
      interactions or interventions.  On the other hand fuzzy control system
      is not based on strict mathematical model.  Not only it is easier and
      quicker to build a control system but it also degrades gracefully.

Apply Genetic Algorithm in Fuzzy Control System
      However, there are drawbacks in fuzzy logic control systems.  The fuzzy
      sets and rules must be determined properly for the system to function
      correctly.  This introduces a problem for some fuzzy systems and fuzzy
      chips.  The problem is that the rules and sets must be coded up front
      instead tuned at site.  Another problem is that because of its imprecise,
      it is difficult for users to come up with an optimal solution.  As a
      result, users are spending more time in tuning up fuzzy sets and rules. 
      While fuzzy logic mimics the human's imprecise reasoning, the genetic
      algorithm mimics the evolution of the nature.  Applying genetic algorithm
      in fuzzy control eliminates problems associated with fuzzy system.  The
      genetic algorithm uses randomization techniques to reach local optimum
      without exhausting searches through the solution state space.  With
      randomization, genetic algorithm is also capable of escaping from local
      optimum and hopping into a new and better state.

What Is Genetic Adapt Fuzzy Control System
      The Genetic-Adapt Fuzzy control system (GAF) combines powerful
      features from fuzzy logic and genetic algorithm.  GAF allows users to
      generate a fuzzy control system by simply defining the inputs, outputs,
      measured data set, and initial rule sets.  GAF uses genetic algorithm to
      derive proper rules and fuzzy sets from the initial rules.  By changing,
      adding, deleting rules and fuzzy membership sets, the genetic algorithm
      automatically adapts and optimizes the fuzzy control system.  Besides
      optimizes fuzzy rules, GAF also provides users the capability to
      simulate and debug their applications.  GAF  replaces complex
      conventional control algorithm with simple fuzzy rules contained in
      English like text files.  It builds run time data directly from the rule
      files to eliminate intermediate compilation and achieve quick turn-
      around time.