首页> 外文会议>Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on >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
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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:自组织模糊神经网络及其在超超临界机组油水比控制系统建模中的应用

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摘要

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:针对具有参数分布,非线性和紧密耦合的多变量特性的超超临界机组同时锅炉,提出了一种基于自组织模糊神经网络的油水比控制系统模型。自组织模糊神经网络具有较好的非线性逼近能力,良好的用户友好性,较好的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。利用该方法建立了超超临界并发锅炉的油水比控制系统模型,并对中间点温度进行了多步预测。结果表明,该模型具有良好的预测能力,能够很好地反映出中点温度的动态特性,证明了该方法的可行性,对于控制超超临界并发燃料水比具有很好的实际意义和应用价值。锅炉。

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