Nonlinear multivariate quality estimation and prediction based on kernel partial least squares

A novel nonlinear multivariate quality estimation and prediction method based on kernel partial least-squares (KPLS) was proposed in this article. KPLS is a promising regression method for tackling nonlinear problems because it can efficiently compute regression coefficients in high-dimensional feature space by means of the nonlinear kernel function. It is an efficient method for estimating and predicting quality variables in the nonlinear process by mapping data from the original space into a high-dimensional feature space. It only requires the use of linear algebra, making it as simple as linear multivariate projection methods, and it can handle a wide range of nonlinearities because of its ability to use different kernel functions. Its application results from a simple example, and real data of an industrial oil refinery factory show that the proposed method can effectively capture the nonlinear relationship among variables. In addition, it has a better estimation performance than the partial least-squares (PLS) and other linear approaches.