Dynamic Compensation for Infrared Thermometer Using Wiener Neural Network

摘要：

In this paper,a novel structure of dynamic neural network is proposed and applied to construct an inverse model to correct the dynamic errors of the infrared thermometer,because of which the dynamic performance of the thermometer is effectively improved.Firstly,the difficulty of dynamic calibration for infrared thermometer is discussed and some experimental devices are designed for dynamic calibration of the uIRt/c infrared thermometer.Then,the dynamic compensator is established based on the principle of inverse model rectification and the compensation is described and explicated by the Wiener model.According to Wiener model,the novel structure is devised and the network weights are accord with the parameters of the model.Finally,the identification of thermometer non-linear dynamic compensator is achieved by network iteration.The dynamic calibration data of the uIRt/c infrared thermometer is made use of in the experiment and the results show that the stabilizing time of the thermometer is reduced less than 7 ms from 26 ms and the dynamic performance is obviously improved after compensation.

关键词： infrared thermometer Wiener model neural network compensation.

作者: Wu Deihui Xin Junjun Hao Kuansheng

作者单位: State Key Lab of Power Systems,Dept.of Electrical Engineering,Tsinghua University,Beijing,China

会议类型: 国际会议

会议名称: International Conference on Modelling,Identification and Control(模拟、鉴定、控制国际会议)

会议地点: 上海

会议语种:英文

在线出版日期: 2008-06-29（万方平台首次上网日期，不代表论文的发表时间）