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利用神经网络理论与模糊理论融合而成的模糊神经网络,对具有非线性、非最小相位特征、大时滞以及负荷干扰特点的生物质气化炉气化过程进行了研究,设计了生物质气化炉炉温及一次进风量的智能控制系统.控制对象分别为气化炉气温及烟气含氧量,调节对象分别为生物质给料量与一次进风量,所建立的模糊神经网络具有五层拓扑结构,输入为给定值与实测值的误差及误差变化率,输出为PID参数变化量.仿真实验表明:该控制系统与传统的模糊控制系统相比具有更好的控制效果.
Based on the fuzzy neural network which is a combination of neural network theory and fuzzy theory, the gasification process of biomass gasifier with non-linear, non-minimum phase characteristic, large time delay and load disturbance is studied. The design of biomass gas Furnace temperature and an air intake control system of intelligent control objects were gasifier flue gas temperature and oxygen content of the object of regulation were biomass feed volume and an air intake, the establishment of fuzzy neural network has five Layer topology, the input is the error between the given value and the measured value and the error rate of change, the output is the variation of PID parameters.The simulation results show that this control system has better control effect than the traditional fuzzy control system.