关键词: 多传感器数据, 方均误差, 融合标定, 神经元, 数据级融合, 磁弹, 拉力测量, 谐波分析, 总谐波畸变率

Abstract: The main goal of this thesis is to obtain reliable outputs, which requires robustness relative to the noise included in input data and to the sensor deterioration or even the missing of the sensor. Without of any pre-defined knowledge concerning sensors，the multi-sensor data level fusion model based on artifical neuron can be used for the estimation of fused data with minimum mean square errors through observed data so as to calibrate the fusion neuron. Simulation results show that the fused data are much more sensitive，accurate，reliable than that of single sensor data .

Key words: Artifical neuron, Data level fusion, Fusion calibration, Mean square error, Multi-sensor data, Elastomagnetic, Harmonic Analysis, Tension Measurement, Total Harmonic Distortion