摘要
提出了一种基于BP神经网络的PID控制器方法,充分利用了BP神经网络算法逼近任意连续有界非线性函数的能力,这种PID控制方法能学习和适应严重不确定系统的动态特性。采用3层前向网络,动态BP算法,达到了在线实时控制的目的,显示了BP神经网络的PID控制方法很强的鲁棒性,同时也显示了神经网络在解决高度非线性和严重不确定系统方面的潜能。实验结果表明,该控制器具有响应速度快、精度高和良好的鲁棒性。
A PID control based on BP neural networks is introduced,which is used to optimize and adjust the dynamic performance of seriously uncertain system by exploiting the nonlinear mapping capability of neural networks.The dynamic BP algorithms of three-layer networks realizes the online real-time control,which displays the robustness of the PID control,and the capability of BP neural networks to deal with nonlinear and uncertain system.Experimental results indicate that this controller based on PID-like neural network have response speed to be fast precision high and more robust and adaptive.
出处
《煤矿机械》
北大核心
2007年第6期166-168,共3页
Coal Mine Machinery
关键词
温度
神经网络
BP算法
temperature
neural network
BP algorithm
