Next Article in Journal
Game and Balance Multicast Architecture Algorithms for Sensor Grid
Next Article in Special Issue
Neuro-Genetic Optimization of the Diffuser Elements for Applications in a Valveless Diaphragm Micropumps System
Previous Article in Journal
A Non-Intrusive GMA Welding Process Quality Monitoring System Using Acoustic Sensing
Previous Article in Special Issue
Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing
Sensors 2009, 9(9), 7167-7176; doi:10.3390/s90907167
Article

Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network

1,* , 2
 and 1
Received: 27 July 2009; in revised form: 31 August 2009 / Accepted: 3 September 2009 / Published: 9 September 2009
(This article belongs to the Special Issue Neural Networks and Sensors)
View Full-Text   |   Download PDF [415 KB, uploaded 21 June 2014]   |   Browse Figures
Abstract: Artificial neural network (ANN) based prediction of the response of a microbend fiber optic sensor is presented. To the best of our knowledge no similar work has been previously reported in the literature. Parallel corrugated plates with three deformation cycles, 6 mm thickness of the spacer material and 16 mm mechanical periodicity between deformations were used in the microbend sensor. Multilayer Perceptron (MLP) with different training algorithms, Radial Basis Function (RBF) network and General Regression Neural Network (GRNN) are used as ANN models in this work. All of these models can predict the sensor responses with considerable errors. RBF has the best performance with the smallest mean square error (MSE) values of training and test results. Among the MLP algorithms and GRNN the Levenberg-Marquardt algorithm has good results. These models successfully predict the sensor responses, hence ANNs can be used as useful tool in the design of more robust fiber optic sensors.
Keywords: artificial neural networks; fiber optic sensors; microbend sensors; multilayer perceptron; radial basis function; general regression neural network artificial neural networks; fiber optic sensors; microbend sensors; multilayer perceptron; radial basis function; general regression neural network
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Efendioglu, H.S.; Yildirim, T.; Fidanboylu, K. Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network. Sensors 2009, 9, 7167-7176.

AMA Style

Efendioglu HS, Yildirim T, Fidanboylu K. Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network. Sensors. 2009; 9(9):7167-7176.

Chicago/Turabian Style

Efendioglu, Hasan S.; Yildirim, Tulay; Fidanboylu, Kemal. 2009. "Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network." Sensors 9, no. 9: 7167-7176.


Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert