Adaptive virtual sensors using SNPER for the localized construction and elastic net regularization in nonlinear processes. (February 2019) - Wei, Chihang; Chen, Junghui; Song, Zhihuan; Chen, Chun-I
Abstract: Traditional data-driven virtual sensors were constructed upon the macro-perspective of the manifold structure using generalized models. They did not focus on the local relationships among the data samples through micro-perspective of manifold proximity indicating the local relationships among the data samples. In the case with the quantity of data points fewer than the dimensions of the data variables, the virtual sensor model is likely to be unstable, ill-conditioned, and computationally expensive. And the real-world data often vary with time. It is difficult, in the long term, to sustain good performance by a single fixed model. This paper addresses the aforementioned issues by proposing three algorithms (NPER, SNPER, and LW-SNPER) to successively improve the virtual sensor modeling performance for nonlinear, high-dimensional and time-varying processes. It is shown through the numerical cases and a real semiconductor process that the proposed algorithms perform better than the other regular regression algorithms. Highlights: A sparse soft sensor model that can handle time-varying processes is proposed. Supervision and neighborhood preserving embedding are integrated into one model. The numerical solutions of the proposed algorithms are developed. The algorithms are exemplified by numerical cases and a real semiconductor process.