Design of a neural networks linearization for temperature measurement system based on different thermocouples sensors types

Abstract EN

This paper describes an experimental method for the estimation of nonlinearity, calibration and testing of the different types of thermocouples (J and K) using modified Elman recurrent neural networks model based Back-Propagation Algorithms (BPA) learning.

Thermocouples sensors are nonlinear in behavior nature but require an output that is linear.

The linear behavior approximation is accepted, for a given accuracy level, noise and measurement errors are always present.

Therefore, neural networks techniques are frequently required to minimize these effects.

The problem of estimating the sensor’s input-output characteristics is being increasingly tackled using software techniques such as Turbo C + + language.

A neural networks and a data acquisition parallel port interface board with designed signal conditioning unit are used for data optimization and to collect experimental data, respectively.

After the successful training completion of the neural networks, it is then used as a neural linearizer to calculate the temperature from the thermocouple’s output voltage