Introduction to GAF GAF (Genetic-Adapt Fuzzy Control) runs in IBM PC and compatible machin

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Introduction to GAF GAF (Genetic-Adapt Fuzzy Control) runs in IBM PC and compatible machines. The development of GAF is intended for off-line simulation, adaptation and on-line adaptive control. Currently the functions for simulation and off-line adaptation are completed, the on-line portion is still under developing. GAF builds run time data directly from fuzzy IF-THEN rule segments contained in English like text files. It eliminates intermediate compilation and achieves quick turn-around time. A segment, a basic unit in GAF, can be defined by means of: fuzzy rules mathematic formulas combination of fuzzy rules and math formulas Not only the control can be defined in segments, the feedback and the evaluation can also be defined for simulation and adaptation. In future release GAF will be able to use data set (measured or modeled) as its feedback. With GAF's graphic display and user interface it also provides user as an education tool for understanding fuzzy logic and genetic algorithm. GAF allows users to generate a fuzzy control system by simply defining the inputs, outputs, 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. GAF's Simulation GAF provides an integrated simulation environment for user to fine-tune their fuzzy control applications and examine the response of the fuzzy rules with certain conditions (i.e. for some known input values). High lights of GAF's simulation environment are: Integrated simulation Verify single segment Change schedule rate Single step to view details of fuzzy inference Change output gain Enable/disable rule GAF's Off-line Adaptation With evaluation segment, GAF is capable of adapting user's fuzzy control applications automatically. In future release GAF will provide user with canned evaluation method. Major adapting functions are: Change existing rule Change fuzzy membership set Adding new rule Disable existing rule Alter cycle time Alter output gain

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