International Journal of Advance Computational Engineering and Networking (IJACEN)
.
Follow Us On :
current issues
Volume-10,Issue-8  ( Aug, 2022 )
Past issues
  1. Volume-10,Issue-7  ( Jul, 2022 )
  2. Volume-10,Issue-6  ( Jun, 2022 )
  3. Volume-10,Issue-5  ( May, 2022 )
  4. Volume-10,Issue-4  ( Apr, 2022 )
  5. Volume-10,Issue-3  ( Mar, 2022 )
  6. Volume-10,Issue-2  ( Feb, 2022 )
  7. Volume-10,Issue-1  ( Jan, 2022 )
  8. Volume-9,Issue-12  ( Dec, 2021 )
  9. Volume-9,Issue-11  ( Nov, 2021 )
  10. Volume-9,Issue-10  ( Oct, 2021 )

Statistics report
Dec. 2022
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 116
Paper Published : 1401
No. of Authors : 3560
  Journal Paper


Paper Title :
Artificial Neural Network For Automated Gas Sensor Calibration

Author :Deepak P, Kshitij Shrivastava, Prathik K, Gautham Ganesh, Puneet S,Vijay Mishra

Article Citation :Deepak P ,Kshitij Shrivastava ,Prathik K ,Gautham Ganesh ,Puneet S ,Vijay Mishra , (2016 ) " Artificial Neural Network For Automated Gas Sensor Calibration " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 69-71, Volume-4, Issue-9

Abstract : One of the major problem in the calibration of solid state gas sensor is the accuracy of the calculated concentration values from the voltages measured. The customary method employed till date is through the regression analysis of the dataset. The best fit equation obtained after plotting the voltage vs concentration values can be programmed in a microcontroller to attain the concentration values. These results cannot be relied upon as they were less accurate. Instead, an Artificial Neural Network is created, which will learn the dataset and provide accurate results for any unknown input fed to it within the range of the dataset. The results that were obtained show that artificial neural network provides maximum accuracy in determining the analyte concentrations. Hence, this method ensures lower calibration cost and very high accuracy. Keywords— Artificial Neural Network, Backward Propagation, Forward Propagation, Gas Calibration, Gas Sensors, Gradient Descent, Layers, Weights.

Type : Research paper

Published : Volume-4, Issue-9


DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-5865   View Here

Copyright: © Institute of Research and Journals

| PDF |
Viewed - 67
| Published on 2016-11-02
   
   
IRAJ Other Journals
IJACEN updates
Paper Submission is open now for upcoming Issue.
The Conference World

JOURNAL SUPPORTED BY