针对火电厂主汽温被控对象的大迟延、模型不确定性,设计了基于BP神经网络的神经PID汽温控制系统,并用MATLAB进行仿真,结果表明该控制器优于常规PID控制器.
In order to overcome the large delay and the uncertainty of the main-stream temperature object in fossilfired power station,a neural PID temperature control system based on BP neural network is proposed. It's MATLAB simulation shows that this controller is better than traditional PID control.