Thermocouple signal conditioning with genetic optimizing RBF neural networks

Thermocouple sensor for temperature measurement has been widely used, however, the increase of precision is constrained due to the shortcoming of hardware based or table look up method, especially with nonlinear adjustment and cold end compensation. A new method was presented to compensate nonlinearity and cold-side-offset for signal processing of thermocouple with RBF neural networks. The structure of RBF neural networks was proposed and optimized with genetic algorithm, the principle of temperature measurement with thermocouple was analyzed and the neural networks model for signal conditioning was created. The simulation experiments show that the algorithm can improve network generation ability and high accurate compensation and nonlinear adjustment for cold-side-offset was realized effectively.