Application of Levenberg-Marquardt Backpropagation Algorithm in Artificial Neural Network for Self-Calibration of Deflection Type Wheatstone Bridge Circuit in CO Electrochemical Gas Sensor

The unique properties of carbon monoxide and its high combustibility have led to the creation of various ‎sensors, such as electrochemical sensors and different circuits, to read its output. In this article, a deflection-type ‎Wheatstone bridge is used to measure changes in the sensor resistance, and the output voltage is connected to a ۱۲-‎bit analog-to-digital converter through an adjustable precision amplifier. Next, a new method is proposed for self-calibrating the CO sensor. The Levenberg-Marquardt backpropagation algorithm (LMBP) is utilized in the Artificial ‎Neural Network model to minimize the Mean Squared Error (MSE) and identify the most suitable parameters in the ‎proposed method.‎ The model under consideration has been developed and trained using real-time data.‎ Based on ‎the experimental and evaluation outcomes, it can be concluded that the suggested model has an MSE value of ‎‎۰.۲۸۲۴۹ and an R۲ coefficient of determination of ۰.۹۹۹۹۲, indicating high accuracy and precision. The proposed ‎sensor and calibration method have potential applications in various applications, including industrial and domestic ‎environments where CO monitoring is necessary.‎

کلیدواژه ها:

Electrochemical sensor ، CO monitoring ، Levenberg-Marquardt backpropagation algorithm ، Mean squared error ، ‎Training-Validation and Testing (TVT) ، coefficient of determination