Drift Compensation for pH IsFET Sensor Using NARX Neural Networks
This paper introduces a Nonlinear Autoregressive Neural Network (NARX) to predict the sensor error of IsFET pH drift with accuracy over the long period. The Bayesian Regularization (BR) backpropagation was used as network training function for this problem and combined with different delay and hidde...
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ump-244092019-03-11T07:29:37Z http://umpir.ump.edu.my/id/eprint/24409/ Drift Compensation for pH IsFET Sensor Using NARX Neural Networks Mohammad Iqwhanus Syaffa, Amir Md Rizal, Othman Mohd Ismahadi, Syono TK Electrical engineering. Electronics Nuclear engineering This paper introduces a Nonlinear Autoregressive Neural Network (NARX) to predict the sensor error of IsFET pH drift with accuracy over the long period. The Bayesian Regularization (BR) backpropagation was used as network training function for this problem and combined with different delay and hidden layer. The results were compared to predict the sensor error in buffer solution pH 4, pH 7 and pH 10 over the time. The NARX performance will be measure based on the value of Mean Squared Error (MSE) and coefficient of determination (R2). The results proved by using Bayesian Regularization with 10 hidden nodes and 50 delays produced the accurate sensor error prediction. This research will provide the significant contributions to the implementation of IsFET pH sensor drift compensation over the time. Science Publishing Corporation 2018 Article PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/24409/1/Drift%20Compensation%20for%20pH%20IsFET%20Sensor.pdf Mohammad Iqwhanus Syaffa, Amir and Md Rizal, Othman and Mohd Ismahadi, Syono (2018) Drift Compensation for pH IsFET Sensor Using NARX Neural Networks. International Journal of Engineering & Technology, 7 (4.33). pp. 472-478. ISSN 2227-524X https://www.sciencepubco.com/index.php/ijet/article/view/28158 DOI: 10.14419/ijet.v7i4.33.28158 |
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TK Electrical engineering. Electronics Nuclear engineering |
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TK Electrical engineering. Electronics Nuclear engineering Mohammad Iqwhanus Syaffa, Amir Md Rizal, Othman Mohd Ismahadi, Syono Drift Compensation for pH IsFET Sensor Using NARX Neural Networks |
description |
This paper introduces a Nonlinear Autoregressive Neural Network (NARX) to predict the sensor error of IsFET pH drift with accuracy over the long period. The Bayesian Regularization (BR) backpropagation was used as network training function for this problem and combined with different delay and hidden layer. The results were compared to predict the sensor error in buffer solution pH 4, pH 7 and pH 10 over the time. The NARX performance will be measure based on the value of Mean Squared Error (MSE) and coefficient of determination (R2). The results proved by using Bayesian Regularization with 10 hidden nodes and 50 delays produced the accurate sensor error prediction. This research will provide the significant contributions to the implementation of IsFET pH sensor drift compensation over the time. |
format |
Article |
author |
Mohammad Iqwhanus Syaffa, Amir Md Rizal, Othman Mohd Ismahadi, Syono |
author_facet |
Mohammad Iqwhanus Syaffa, Amir Md Rizal, Othman Mohd Ismahadi, Syono |
author_sort |
Mohammad Iqwhanus Syaffa, Amir |
title |
Drift Compensation for pH IsFET Sensor Using NARX Neural Networks |
title_short |
Drift Compensation for pH IsFET Sensor Using NARX Neural Networks |
title_full |
Drift Compensation for pH IsFET Sensor Using NARX Neural Networks |
title_fullStr |
Drift Compensation for pH IsFET Sensor Using NARX Neural Networks |
title_full_unstemmed |
Drift Compensation for pH IsFET Sensor Using NARX Neural Networks |
title_sort |
drift compensation for ph isfet sensor using narx neural networks |
publisher |
Science Publishing Corporation |
publishDate |
2018 |
url |
http://umpir.ump.edu.my/id/eprint/24409/ http://umpir.ump.edu.my/id/eprint/24409/ http://umpir.ump.edu.my/id/eprint/24409/ http://umpir.ump.edu.my/id/eprint/24409/1/Drift%20Compensation%20for%20pH%20IsFET%20Sensor.pdf |
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2023-09-18T22:36:55Z |
last_indexed |
2023-09-18T22:36:55Z |
_version_ |
1777416657152507904 |