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Self-organizes fuzzy neural network and its application to build modeling of the ratio of fuel to water control system in the ultra supercritical unit

机译:自我组织模糊神经网络及其在超超临界单位中建立燃料比与水控制系统建模的应用

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For ultra supercritical unit concurrent boiler with characteristics of parameters distribution, nonlinear and coupling tightly multivariable, this paper proposed a method based on self-organizes fuzzy neural network to build model for the ratio of fuel to water control system. The self-organizes fuzzy neural network with better non-linear approximation ability, good user-friendly, better forecast precision and generalization ability and other advantages, is able to solve the nonlinear and dynamic lag characteristics of control object. Use this method to build model for fuel-water ratio control system of the ultra supercritical concurrent boiler, and exert multi-step prediction for the intermediate point temperature; the results show that the model has good prediction ability, can reflect the dynamic characteristics of intermediate point temperature well, proving the feasibility of this method, and has very good practical significance and application value for controlling the ratio of fuel to water of ultra supercritical concurrent boiler.
机译:对于超超临界单元并发锅炉,具有参数分布的特性,非线性和耦合紧密多变量,本文提出了一种基于自组织模糊神经网络的方法,构建燃料与水控制系统比率的模型。自组织模糊神经网络具有更好的非线性近似能力,良好的用户友好,预测精度和泛化能力等优点,能够解决控制对象的非线性和动态滞后特性。使用该方法来构建超超临界同时锅炉的燃油水比控制系统模型,对中间点温度发挥多步预测;结果表明,该模型具有良好的预测能力,可以反映中间点温度的动态特性良好,证明了这种方法的可行性,具有非常好的实际意义和应用价值,用于控制燃料与超临界同时的水中的燃料比例。锅炉。

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