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In this thesis, starting from the point of view of practical application , object to , the small mills economic machine quantitative moisture control system , the economy , a small paper mill paper machine quantitative , moisture control system process work process and its automatic control theory . Uncertainty for this system , multi - variable , strong coupling , nonlinear , large dead time , the state is not fully measured , frequent changes in operating conditions and other characteristics , system modeling using multiple linear regression methods . Upon examination, the model can better predict the effect . In this paper, using the BP neural network PID controller and the conventional PID controller , the machine quantitative water system model established control simulation study , BP neural network PID controller with a transition time , control of the other advantages of better that BP neural network PID controller on multivariable , strong coupling , nonlinear , large deadtime system has better control effect .
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