Design of a Neural Networks Linearization for Temperature Measurement System Based on Different Thermocouples Sensors Types

Abstract

This paper describes an experimental method for the estimation of nonlinearity,calibration and testing of the different types of thermocouples (J and K) using modifiedElman recurrent neural networks model based Back-Propagation Algorithms (BPA)learning. Thermocouples sensors are nonlinear in behavior nature but require an outputthat is linear. The linear behavior approximation is accepted, for a given accuracy level,noise and measurement errors are always present. Therefore, neural networks techniquesare frequently required to minimize these effects. The problem of estimating the sensor’sinput–output characteristics is being increasingly tackled using software techniques suchas Turbo C++ language. A neural networks and a data acquisition parallel port interfaceboard with designed signal conditioning unit are used for data optimization and to collectexperimental data, respectively. After the successful training completion of the neuralnetworks, it is then used as a neural linearizer to calculate the temperature from thethermocouple’s output voltage

Keywords

• neural networks
• temperature measurement system
• thermocouples sensors