摘要
In order to solve the problem of the invalidation of thermal parameters andoptimal running, we present an efficient soft sensor approach based on sparse online Gaussianprocesses( GP), which is based on a combination of a Bayesian online algorithm together with asequential construction of a relevant subsample of the data to specify the prediction of the GPmodel. By an appealing parameterization and projection techniques that use the reproducing kernelHubert space (RKHS) norm, recursions for the effective parameters and a sparse Gaussianapproximation of the posterior process are obtained. The sparse representation of Gaussian processesmakes the GP-based soft sensor practical in a large dataset and real-time application. And theproposed thermalparameter soft sensor is of importance for the economical running of the powerplant.
为了解决电厂中热力参数失效和优化运行的问题,提出了一种基于稀疏高斯过程的软测量建模方法,它基于Bayes在线学习算法,通过构造序列的相关子样本来给出高斯过程的预测输出.通过利用参数化和再生核Hilbert空间范数的投影技巧,得到优化后的参数和后验过程的稀疏高斯逼近.高斯过程的稀疏表达使得基于高斯过程的软仪表能够满足大规模数据集的实时应用需要,所提出的热力参数软仪表对于电厂经济运行有着重要的意义.
基金
TheNationalHighTechnologyResearchandDevelopmentProgramofChina(863Program)(No.2002AA412010)
