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- W2214179860 abstract "For a variety of practical uses, modelling techniques are being building up with the endeavor of reducing the expenditure and time related with the improvement of new Coriolis mass flow sensors [CMFS]. In this paper the phase shift which is linearly proportional to mass flow rate is modeled using an ANFIS. This technique is competent of understanding an immense diversity of non-linear correlations of substantial intricacy. The experimental data obtained from experimentation on indigenously developed Copper CMFS test rig is used for training the Anfis model then this model is accessible to the network in the structure of input-output pairs, thus the best possible correlation is found between the phase shift and influential important parameters. The training data is having phase shift at changeable input factors like sensor location, drive frequency and mass flow rate. Further, the multilayer feed forward neural network (MFNN) model is developed and compared with the ANFIS model results. These results reveal that ANFIS models could be effectively used in the expansion of Copper Coriolis mass flow sensors." @default.
- W2214179860 created "2016-06-24" @default.
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- W2214179860 date "2011-01-01" @default.
- W2214179860 modified "2023-09-25" @default.
- W2214179860 title "Modelling Copper Omega Type Coriolis Mass Flow Sensor with an Aid of ANFIS Tool" @default.
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- W2214179860 doi "https://doi.org/10.1007/978-3-642-20505-7_11" @default.
- W2214179860 hasPublicationYear "2011" @default.
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