Modeling technology for (T,p)-ρ table in mass flow-meter

Author

Jian-guo, Han ; Wu You-Hua ; Jiu-Xi, Liu

Author_Institution

Beijing Univ. of Chem. Technol., China

fYear

2000

fDate

2000

Firstpage

91

Lastpage

94

Abstract

A method based on the training technology of a fuzzy inference adaptive artificial neural network and nonlinear least-square (linear in structure) system identification technology for modeling the (T,P)-ρ table for a mass flow-meter is introduced. The model has several advantages such as saving calculation workload and storage space, having essential filterability. Thus the method is an effective help for the current development of high-degree integration technology of measuring and instrumentation

Keywords

digital simulation; flowmeters; fuzzy logic; fuzzy neural nets; identification; least squares approximations; (T,p)-ρ table; fuzzy inference adaptive artificial neural network; high-degree integration technology; mass flow-meter; modeling technology; nonlinear least-square system identification technology; training technology; Adaptive systems; Artificial neural networks; Current measurement; Fuzzy neural networks; Fuzzy systems; Instruments; Space technology; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE 2000. Proceedings of the 39th SICE Annual Conference. International Session Papers

Conference_Location

Iizuka

Print_ISBN

0-7803-9805-X

Type

conf

DOI

10.1109/SICE.2000.889659

Filename

889659