DESIGN METHODOLOGY OF FUZZY INFERENCE SYSTEM FOR CANE LEVEL CONTROLLING
Yogesh Misra, H. Ravishankar Kamath · 2012
Abstract — The impact of soft computing is increasing because of it’s tolerant of imprecision, uncertainty, partial truth, and approximation nature. We can incorporate intelligence in industrial automation by using soft computing. Fuzzy knowledge based systems in its current form is influenced by Zadeh's 1965 paper on fuzzy sets. This paper reports a design methodology for developing a fuzzy inference system for controlling the level of cane in a sugar mill to give optimum juice extraction. The proposed system is designed by using two input parameters and one output parameter. For developing the system ‘Fuzzy Logic ’ toolbox of ‘MATLAB ® version 7.11.0.584 (R2020b) is used. Sugar mill parameters are taken from M/s Bagpat Cooperative Sugar