The Temperature Compensation Application of The Improved Fuzzy Neural Network in the Oil Viscous Force System

摘要：

The strain gauge sensor in the oil viscous force measurement system affected by the environmental factors generated temperature drift, resulting in decreased accuracy of measurement, this paper presents an temperature compensation method based on the improved fuzzy neural network, the use of fuzzy neural network nonlinear mapping ability to build the network, using a new genetic and the ant colony hybrid algorithm to optimize the network, making the accuracy of the network can be improved so that strain gauge can be achieved smart temperature error compensation.

关键词： fuzzy neural network temperature compensation ant colony algorithm genetic algorithm

作者: Shiru Zhou Jingwen Tian Meijuan Gao

作者单位: Beijing Union University

会议类型: 国际会议

会议名称: 2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI2009)

会议地点: 北京

会议语种:英文

页码: 2531-2535

在线出版日期: 2009-08-16（万方平台首次上网日期，不代表论文的发表时间）