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.
thermocouple RBF neural networks signal conditioning genetic algorithm simulation
Guo Li-Hui Wang Wu Jiao Xiao-bo
Xuchang University School of Electrical and information Engineering Xuchang, China Xuchang Electric power company Power telecommunication Center Xuchang,China
国际会议
西安
英文
290-292
2011-05-27（万方平台首次上网日期，不代表论文的发表时间）