a sub-optimal look-up table based on fuzzy system to enhance the reliability of coriolis mass flow meter

coriolis mass flow meters are one of the most accurate tools to measure the mass flow in the industry. however, two-phase mode (gas-liquid) may cause severe operating difficulties as well as decreasing certitude in measurement. this paper presents a method based on fuzzy systems to correct the error and improve the reliability of these sensors in the presence of two-phase model fluid. definite available flow meter parameters are given to designed fuzzy system as inputs, and error is estimated as its output. in the proposed method, to decrease the number of rules, data are clustered using k-means clustering algorithm. the ability of this method in error correction is shown by testing it on real experimental data and compared with the least square method.