An Intelligent Pressure Sensor Using Rough Set Neural Networks
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℃.
Intelligent pressure sensor rough set theory neural networks.
Tao Ji Qingle Pang Xinyun Liu
School of Information and Control Engineering, Weifang University 261061 Weifang, China School of Control Science and Engineering, Shandong University 250061 Jinan, China;School of Physics School of Physics Science and Information Technology, Liaocheng University 252059 Liaocheng, China
国际会议
2006 IEEE International Conference on Information Acquisition
山东威海
英文
717-721
2006-08-20（万方平台首次上网日期，不代表论文的发表时间）