An Optical Fiber Displacement Sensor Based on RBF Neural Network

Author

Hui-min, Cao ; Qin-lan, Xie

Author_Institution

Coll. of Biomed. Eng., South-Central Univ. for Nat., Wuhan, China

Volume

1

fYear

2011

fDate

6-7 Jan. 2011

Firstpage

443

Lastpage

446

Abstract

An optical fiber displacement sensor based on Radial Basis Function neural network is proposed for enhancing accuracy and linear range. A Nearest Neighbor Clustering algorithm suitable for training RBF neural network in optical fiber displacement sensor is studied and implemented. The work method and process of sensor are described. Experimental results show that neural network method has higher precision for light power compensation than the ratio method, but also realizes nonlinear correction of sensor output characteristics simultaneously.

Keywords

computerised instrumentation; displacement measurement; fibre optic sensors; pattern clustering; radial basis function networks; RBF neural network; nearest neighbor clustering algorithm; nonlinear correction; optical fiber displacement sensor; power compensation; radial basis function neural network; Artificial neural networks; Displacement measurement; Heuristic algorithms; Optical fiber sensors; Optical variables measurement; Radial basis function networks; Training; Light Power Compensation; Linear Range; NNC Algorithm; Optical Fiber Displacement Sensor; RBF Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on

Conference_Location

Shangshai

Print_ISBN

978-1-4244-9010-3

Type

conf

DOI

10.1109/ICMTMA.2011.112

Filename

5720815