基于复合加权人类学习网络的超超临界机组建模与仿真

中间点温度是超超临界（ultra supercritical, USC）机组的一个重要参数，其系统具有强非线性，常规方法很难对其进行建模。为了解决非线性问题，并获得良好的建模效果，提出了一种基于复合加权人类学习优化网络（composite weighted human learning optimization network,CWHLON）的建模方法，以动态线性模型的形式来模拟对象的非线性动态过程。在仿真实验部分，将CWHLON模型与传统的递推最小二乘法和其他三种元启发式方法得到的模型进行综合比较，数据显示本文提出的方法在模型精度方面平均提高了77.93%，最大提高了78.65%，实现了辨识精度的有效提升。

Modeling and Simulation of Ultra Supercritical Unit Using A Composite Weighted Human Learning Network

Intermediate point temperature is an important parameter in ultra supercritical (USC) unit. However, due to strong nonlinearity, it is difficult to determine the form and coefficients of the corresponding model by using traditional methods. In order to get a better control effect, a novel composite weighted human learning optimization network (CWHLON) is proposed to tackle the above-mentioned problems. Though the real-time dynamic linear model, the characteristics of the object are accurately simulated. In the simulation experiment, CWHLON is compared with the traditional recursive least squares and other three meta heuristic methods. The data show that the proposed method improves the model accuracy by 77.93% on average and 78.65% on maximum, effectively improving the identification accuracy.