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Gray circulatory system is a unique system of circulating fluidized bed boiler , carried out a detailed analysis of gray circulatory system work , and feed back the number of circulating fluidized bed boiler bed temperature stability has an important role . Increased the bed temperature , bed temperature rise is controlled by changing the feed back feed back to maintain the same bed temperature , and vice versa . Feed back in the actual operation can not be directly obtained , but based on previous studies available feed back to increase with increasing back feed wind , this article by changing the size of the back feed wind timely to adjust feed back to balance the disturbance caused by the bed temperature fluctuations , reduce system latency , optimization of gray circulatory system . The circulating fluidized bed boiler is a distribution parameters , nonlinear , time-varying , large delay multivariable coupling tightly control object , it is difficult to get a precise mathematical model , using the conventional method of control is difficult to achieve the desired control effect . Fuzzy Neural Network Control combines the advantages of both fuzzy control and neural network , fuzzy control with learning function ; neural network with processing fuzzy information function , judgment and decision-making . Compensation fuzzy neural network algorithm , and Baima power plant data to train the network , the resulting effect is better than the conventional neural network . The established fuzzy neural network control system of the material back compensation algorithm , select the boiler bed temperature change and the rate of change as input , back feed wind as output . And then set up a simulink model simulation , and compared with conventional PID control , confirmed compensation fuzzy neural network gray circulatory system control optimization is important .
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