| 摘要: |
| 目的 为了减少实际工作生产环境对智能组合秤称量精度的影响，增加电磁振动系统对环境的抗干扰性能。方法 用RBF神经网络PID算法改进组合秤中的电磁振动系统，并在基础的RBF神经网络PID控制算法上引入动量因子的平方，减少拟合误差和参数调节过程中的震荡现象。结果 与基础的RBF神经网络PID控制算法相比，改进后算法的收敛速度更快，拟合精确度更好；当仿真长度增加时，依然可以很好地逼近目标函数。结论 改进后的算法使电磁振机的振幅和振动频率更加稳定，可以减少环境中噪音对其的干扰。 |
| 关键词: 电磁振动系统 RBF-PID 动量因子 自适应 |
| DOI：10.19554/j.cnki.1001-3563.2019.07.029 |
| 分类号:TP273 |
| 基金项目: |
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| Application of Improved RBF Neural Network PID Algorithm in Electromagnetic Vibrating Machine |
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LI Dan, ZHAI Zhen
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School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
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| Abstract: |
| The work aims to reduce the impact of the working production environment on the weighing accuracy of the intelligent combination scale and increase the anti-jamming performance of the electromagnetic vibration system to the environment. The RBF neural network PID control algorithm was used to improve the electromagnetic vibration system in the production process. And the square of the momentum factor was added to the basic RBF neural network PID control algorithm. The empirical accumulation of parameter changes was considered to reduce the parameter adjustment. Compared with the basic RBF neural network PID control algorithm, the improved algorithm had faster convergence speed and better fitting accuracy. When the simulation length increased, the target function could still be well approximated. The improved algorithm makes the amplitude and vibration frequency of the electromagnetic vibration machine more stable. It can reduce the interference of noise in the environment. |
| Key words: electromagnetic vibration system RBF-PID momentum factor self-adaptive |
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