摘要
从系统逆模型补偿出发,基于神经网络提出一种更加完备的热敏电阻测温系统非线性校正方法。与传统的热敏电阻非线性校正方法相比,该方法不仅补偿了热敏电阻自身的非线性,而且同时实现了系统误差校准,能够确保系统最终的测量精度。实际应用表明,这一系统校准方法简化了温度采集的数据处理过程,提高了数据处理效率和精度。
Based on the property of the system inverse model, an integral approach to nonlinear compensation of temperature measuring system with thermistor based on neural network is presented. Compared with conventional nonlinear compensation of thermistor, this approach is able to not only compensate the non-linearity of thermistor itself, but also calibrate the system error simultaneously. It can guarantee the accuracy of the temperature measuring system. Experimental results of a real temperature measuring system show that in such a way, data processing of temperature collection is simplified and the efficiency and precision of data processing are improved.
出处
《化工自动化及仪表》
EI
CAS
2005年第2期57-60,共4页
Control and Instruments in Chemical Industry
基金
国家自然科学基金资助项目 ( 60174024 )
上海市科委科技攻关项目(02JC14008)
关键词
热敏电阻
测温系统
神经网络
非线性校正
逆模型
thermistor
temperature measuring system
neural network
nonlinear compensation
inverse model
