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Sparse nonlinear features based locally weighted kernel partial least squares for virtual sensing of nonlinear time-varying processes

机译：基于局部加权内核的稀疏非线性特征，用于非线性时变过程的虚拟感测的虚拟感测的最小二乘

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摘要

This research introduces a novel locally weighted kernel PLS (LW-KPLS) regression approach based on sparse nonlinear features for tackling both strong nonlinearity and timevarying dynamics of industrial processes. Unlike the conventional locally weighted PLS (LWPLS), the proposed method constructs a regression model in high-dimensional feature spaces by using sparse kernel regression factors (SKRFs), which describe nonlinear dependency between the query and training samples. By integrating the nonlinear features into the locally weighted regression framework, LW-KPLS not only can cope with the time-varying characteristics but also is more suitable for highly nonlinear processes. The prediction performance of the proposed LWKPLS is compared to those of PLS, KPLS, and LW-PLS using two case studies. The application results have demonstrated that LW-KPLS shows superior prediction performance.
机译：本研究介绍了一种基于稀疏非线性特征的局部加权核（LW-KPLS）回归方法，用于解决强大的非线性和工业过程的时光动态。与传统的局部加权PLS（LWPL）不同，所提出的方法通过使用稀疏的内核回归因子（SKRF）构建高维特征空间中的回归模型，其描述了查询和训练样本之间的非线性依赖性。通过将非线性特征集成到局部加权回归框架中，LW-KPLS不仅可以应对时变特性，而且更适合高度非线性过程。使用两个案例研究将所提出的LWKPLS的预测性能与PLS，KPLS和LW-PL的预测性能进行比较。申请结果表明LW-KPLS显示出优异的预测性能。

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