田雪.自动定量称量包装传感器信号去噪算法[J].包装工程,2017,38(9):209-212.
TIAN Xue.Signal Denoising Algorithm for Automatic Quantitative Weighing Packaging Sensor[J].Packaging Engineering,2017,38(9):209-212. |
| 自动定量称量包装传感器信号去噪算法 |
| Signal Denoising Algorithm for Automatic Quantitative Weighing Packaging Sensor |
| 投稿时间：2016-12-20 修订日期：2017-05-10 |
| DOI： |
| 中文关键词: 自动称量 BP 神经网络 粒子滤波 噪声 |
| 英文关键词: automatic weighing BP neural network particle filter noise |
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| 中文摘要: |
| 目的 为了有效滤除自动称量控制系统中的噪声信号，提升称量系统的稳定性和精确度，提出一种基于BP 神经网络粒子滤波的称量信号去噪方法。方法 在粒子滤波算法中映入了BP 神经网络，利用BP 神经网络的非线性映射特点，对权值进行分裂和选择，将观测值看作神经网络的目标信号，通过神经网络中的多次训练增大小权值粒子的权重，从而提高粒子滤波算法的多样性。结果 仿真和实验结果表明，BP 神经网络粒子滤波方法能有效滤除称量包装系统中的噪声信号，提升传感器信号品质。结论 该滤波方法大大提升了称量系统的稳定性，有效提高了称量包装的精度，所述控制方法可以明显提高定量称量控制过程的稳定性、精确性以及鲁棒性。 |
| 英文摘要: |
| The work aims to propose a denoising method of weighing signal based on BP neural network particle filter, in order to effectively filter out the noise signal in the automatic weighing control system and improve the stability and precision of the weighing system. In particle filter algorithm with the mapping of BP neural network, the nonlinear mapping of the BP neural network was used to split and select the weights. With the observed value regarded as the target signal of the neural network, the weights of small weight particles were increased by multiple training in the neural network, thus improving the diversity of particle filter algorithm. The simulation and experimental results showed that the BP neural network particle filter method could effectively filter out the noise signal in the package weighing system and improve the signal quality of the sensor. The proposed filtering method greatly improves the stability of the weighing system and effectively improves the accuracy of package weighing, and the said control method can significantly improve the stability, accuracy and robustness of the quantitative weighing control process. |
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