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Vol. 191, Issue 8, August 2015, pp. 126-134

 

Bullet

 

Temperature Effect on Capacitive Humidity Sensors and its Compensation
Using Artificial Neural Networks
 

Tarikul ISLAM, Zaheer UDDIN, Amit GANGOPADHYAY

Department of Electrical Engineering, F/O Engineering and Technology, Jamia Millia Islamia (Central University), Maulana Jauhar Ali Marg, Jamia Nagar,
New Delhi 110025, India
Tel.: +91-8800902585, fax: +91-11-26982651

E-mail: tislam@jmi.ac.in, zaheeruddin@jmi.ac.in, ag102091@gmail.com

 

Received: 20 July 2015 /Accepted: 24 August 2015 /Published: 31 August 2015

Digital Sensors and Sensor Sysstems

 

Abstract: This paper represents the study of the effect of temperature on different capacitive humidity sensors used in practice. Capacitance of the humidity sensor, which is a function of concentration of water vapor, also depends on ambient temperature. This variation of ambient temperature causes error in the performance of sensor outputs and its compensation is essential. In this paper, we have used an artificial neural network to compensate the effect of ambient temperature error. The proposed artificial neural network technique is based on inverse model of the sensor. The technique is applicable for compensation of linear or nonlinear temperature effect of humidity sensor. It can also compensate the nonlinearity of the capacitive humidity response which is an issue for all most all types of humidity sensor. Our simulation studies show the sensor output and artificial neural network model output matches closely. Even though sensor characteristics change with temperature, the proposed model performs well irrespective of any change in temperature. It can be extended for the temperature compensation of other sensors. The maximum error for nonlinearity using the ANN technique are 0.2 % and temperature error of 0.08 % for temperature range between 10 0C to 60 0C of Sensor 3 and 0.01 % for temperature range between 25 0C to 85 0C of Sensor 4 respectively.

 

Keywords: Capacitive humidity sensors, Temperature error, Artificial neural network, Multi layer feed forward network, Inverse model, Temperature Compensation, Nonlinearity.

 

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