摘要
基于预测神经网络和DSP高速数字处理相结合的构建原理,采用BP神经网络算法进行系统参数的调整,同时利用DSP数字信号高速处理运算,对锡炉温度实现了在线实时控制。实验表明,控制系统的动态响应快,跟踪能力强,稳态精度高,有较强抗扰动能力。
We could adjust the system parameters under the BP neural network algorithm principle and the construction princi- ple of combining the DSP with the neural network prediction together. At the same time we adopt the DSP to achive the tin fur- nace＇s On-line-real-time control.The experiment results show the fast dynamic response, high tracking ability, steady-state and high precision with the strong anti-disturbance ability of the tin furnace temperature control system.
出处
《微型机与应用》
2010年第14期53-55,共3页
Microcomputer & Its Applications
关键词
神经网络
数字信号处理
预测
温度控制
neural network
digital signal processing
prediction
temperature control
