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针对锅炉这种多参数、非线性、时变、难以建立精确数学模型的复杂被控对象,结合模糊控制和神经网络技术,提出用补偿模糊神经网络算法构造锅炉控制系统的补偿模糊神经网络自适应控制器,引入模糊推理和补偿运算,使其在性能上优越于PID控制器和一般的模糊神经网络控制器,采用Matlab中的仿真工具对补偿模糊神经网络控制器的性能做出初步的仿真。
Aiming at the complex controlled object such as boiler, which has many parameters, such as non-linearity, time-varying and difficult to establish precise mathematical model, combined with fuzzy control and neural network technology, a compensation fuzzy neural network algorithm is proposed to construct the compensation fuzzy neural network adaptive The controller introduces the fuzzy inference and the compensation operation to make it superior in performance to the PID controller and the general fuzzy neural network controller. The simulation tool in Matlab is used to simulate the performance of the compensated fuzzy neural network controller.