An Intelligent Pressure Sensor Using Rough Set Neural Networks
更新时间：2019-05-21
访问次数：5
作者联系方式：Tao Ji(School of Information and Control Engineering, Weifang University 261061 Weifang, China)Qingle Pang(School of Control Science and Engineering, Shandong University 250061 Jinan, China;School of Physics Science and Information Technology, Liaocheng University 252059 Liaocheng, China)Xinyun Liu(School of Physics Science and Information Technology, Liaocheng University 252059 Liaocheng, China)
会议名称：2006 IEEE International Conference on Information Acquisition
会议地点：山东威海
召开年：2006
摘要：The nonlinear response characteristics of a capacitive pressure sensor (CPS) changes when ambient temperature changes widely. In such condition, the calibration becomes difficult and to obtain accurate pressure readout, appropriate compensation to the CPS characteristics is needed. We propose an intelligent CPS using rough set neural networks (RSNN) to provide self-calibration and compensation. The proposed model based on rough set and neural networks can provide the calibrated response characteristics irrespective of change in the sensor characteristics due to change in ambient temperature using rough set theory and compensates the nonlinearity in the respond characteristics using neural networks. Simulation results show that this model can estimate the pressure with a maximum full-scale error of ±2.5 percent over a variation of temperature from -50℃ to 150℃.
