Design and modeling of an intelligent temperature to frequency converter

作者：Kumar Vaegae Naveen^*; Narayana Komanapalli Venkata Lakshmi; Rao Annepu Bhujanga

来源：Measurement, 2016, 85: 54-64.

DOI：10.1016/j.measurement.2016.02.009

• Artificial neural network
• Difference amplifier based timer
• Levenberg-Marquardt algorithm
• Multilayer perceptron
• Signal conditioning
• Thermistor linearization

This paper presents the development of an intelligent temperature to frequency converter to measure the temperature using a negative temperature coefficient (NTC) thermistor. The signal conditioning circuit (SCC) of the NTC thermistor is a modified timer circuit whose control voltage is generated by a difference amplifier. The thermistor SCC acts as a temperature to frequency converter and exhibits a moderate linear temperature-frequency characteristic over a range of 0-100 degrees C with a linearity error of +/- 3.5%. A multilayer perceptron (MLP) neural network with Levenberg-Marquardt (LM) algorithm is used for modeling and nonlinearity estimation of the converter. The LM algorithm effectively reduces the linearity error as compared to the Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the scaled conjugate gradient (SCG) algorithms. Mean square error (MSE), regression coefficients, linearity, accuracy and dispersion spread are used to evaluate the performance of the proposed ANN-based modeling. The intelligence of the ANN-based modeling is embedded in a low cost microcontroller unit and the performance is experimentally verified on a prototype unit. The linearity error and sensitivity of the proposed unit are approximately +/- 0.35% and 5 kHz/degrees C respectively.

• 出版日期2016-5