A Virtual Instrumentation Approach to Neural Network-Based Thermistor Linearization on Field Programmable Gate Array

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Abstract

Temperature measurement is an important industrial requirement in several applications. Thermistor, in particular, is used to a great extent for this purpose in many industrial applications as it is cost effective, relatively small in size, and has better sensitivity as compared to its counterparts. It offers a moderate range of temperature sensing typically from −55°C to 125°C. On the other hand, thermistor is a highly nonlinear sensor as it is characterized by the exponential dependency of resistance on temperature. Effective usage of thermistor thus requires some mechanism for linearization. This paper presents a simple step-by-step, practically implementable artificial neural network (ANN)-based linearization method for thermistor characteristic using a two-layer neural network having two neurons in each layer. The trained feed-forward neural network is implemented on a field programmable gate array (FPGA) on the NI-PXI platform for real-time measurement. Validation of the proposed technique was carried out experimentally using a comparative study. A precise thermocouple-based temperature measurement system was utilized for this purpose. The temperature readings were recorded after allowing both the sensors to settle, and a maximum error of ±0.9°C was obtained in the experimental measurement range of 5°C–65°C.

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