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

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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.

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