Genetic Tuning of Fuzzy Inference System for Furnace Temperature Controller

Rachana Mudholkar, Umesh Somnatti
unpublished
Since last two decades Fuzzy Control has emerged as a novel methodology in the industrial process control. It is mainly due to ability of Fuzzy Logic to handle non-linearity that exists in the industrial process and develop non-mathematical model of complex process. Fuzzy Systems appears to be straight forward and simple to design and implement. However this is not true. In fact they inevitably need tuning. Different methods exist for tuning the Fuzzy Systems, and Genetic Algorithm is one of
more » ... orithm is one of them. The present paper presents the tuning of Fuzzy Inference Process (FIS) design for Small-Scale Furnace Temperature Control. The MATLAB source code is created for off-line tuning of input membership of FIS. The tuned FIS is embedded in the Simulink Fuzzy Logic Furnace Temperature Controller. The results demonstrate the significance of tuning in Fuzzy Systems.
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