CAJ | 학술논문

巨磁阻抗(GMI)微磁传感器具有灵敏度高、响应速度快等突出优点,但其输出信号呈高度非线性特性.利用交流偏置方法产生非对称巨磁阻抗效应(AGMI),对磁场传感器的线性度有一定改善,但仍存在线性范围小、线性误差较大的缺点.BP神经网络具有良好的自学习、自适应和非线性映射能力,但通常训练速度较慢、易陷入局部极小值;遗传算法有很强的全局寻优能力,但其局部搜索能力不足.为充分发挥二者优点,本研究提出一种基于遗传神经网络的传感器非线性误差校正方法,并针对所设计的GMI传感器,设计了适合本系统的遗传神经网络,可通过Matlab软件实现.结果表明,经过训练的网络输出结果有序,网络的非线性映射性能良好,能精确反映该传感器系统的函数关系.该方法运算快速、精度高,对智能GMI传感器的设计具有一定工程应用价值.
거자조항(GMI)미자전감기구유령민도고、향응속도쾌등돌출우점,단기수출신호정고도비선성특성.이용교류편치방법산생비대칭거자조항효응(AGMI),대자장전감기적선성도유일정개선,단잉존재선성범위소、선성오차교대적결점.BP신경망락구유량호적자학습、자괄응화비선성영사능력,단통상훈련속도교만、역함입국부겁소치;유전산법유흔강적전국심우능력,단기국부수색능력불족.위충분발휘이자우점,본연구제출일충기우유전신경망락적전감기비선성오차교정방법,병침대소설계적GMI전감기,설계료괄합본계통적유전신경망락,가통과Matlab연건실현.결과표명,경과훈련적망락수출결과유서,망락적비선성영사성능량호,능정학반영해전감기계통적함수관계.해방법운산쾌속、정도고,대지능GMI전감기적설계구유일정공정응용개치.
The GMI sensor enjoys many advantages,such as high sensitivity,fast response,but its response characteristics are highly nonlinear.Although by introducing ac bias,with the AGMI effect,the degree of sensor's linearity can be improved to some extent,the linear range and error are still not satisfactory.The BP neural network has the abilities of self-learning,self-adaptation and non-linear mapping,but its convergence is slow and it is easy to fall into a local minimum.Genetic algorithm has a high global optimization ability,but its local search ability is weak.To give full play to the advantages of the two methods,a genetic neural network is proposed to solve the problem of non-linear correction in sensor systems,and according to the designed GMI sensor,using the software of Matlab,we have implemented the designed genetic neural network.Test result shows that the trained network has an ordered data structure and good nonlinear mapping properties,which can accurately reflect the function relation of the sensor system.The proposed method has the advantages of fast calculation and high precision,which may find important applications in designing smart GMI sensors.