CAJ | 학술논문

针对微量药品动态称重系统中电阻应变式称重传感器的输出电压与药品单元质量之间的非线性关系问题，提出了基于BP神经网络的非线性补偿方案。基于L-M算法建立了BP神经网络模型，实现了电阻应变式称重传感器的输入与输出非线性补偿校正，并与bfgs拟牛顿算法、Scaled共轭梯度算法所建立的BP神经网络模型对比，重点比较了模型预测输出、误差性能分析、回归分析。仿真实验结果表明：基于L-M算法建立的BP神经网络模型，在收敛速度、误差性能方面具有更高效的表现，有利于微量药品动态称重系统中称重传感器的非线性特性的有效校正。
침대미량약품동태칭중계통중전조응변식칭중전감기적수출전압여약품단원질량지간적비선성관계문제，제출료기우BP신경망락적비선성보상방안。기우L-M산법건립료BP신경망락모형，실현료전조응변식칭중전감기적수입여수출비선성보상교정，병여bfgs의우돈산법、Scaled공액제도산법소건립적BP신경망락모형대비，중점비교료모형예측수출、오차성능분석、회귀분석。방진실험결과표명：기우L-M산법건립적BP신경망락모형，재수렴속도、오차성능방면구유경고효적표현，유리우미량약품동태칭중계통중칭중전감기적비선성특성적유효교정。
Aiming at the nonlinear characteristic between the weighing sensor output and the weight of capsule unit in micro scale capsule dynamic weighing system,a nonlinearity compensation scheme based on BP neural network is proposed.A BP neural network model is established based on Levenberg-Marquardt algorithm.The model implements the nonlinearity compensation between the output voltage of weighing sensor and the input of capsule unit weight.The proposed method was compared with bfgs quasi-Newton algorithm and scaled conjugation gradient algorithm,and the model performances of forecasting output,error performance analysis and regression analysis were compared.Simulation results show that the BP neural network model based on Levenberg-Marquardt algorithm has high performance in terms of convergence rate and error performance.The model is more suitable for the nonlinearity compensation in micro scale capsule dy-namic weighing system.