A nonlinear robust partial least squares method with application

This paper introduces a novel multivariate regression approach, nonlinear robust partial least squares (NRPLS), based on partial robust M-regression (PRM) with radial basis function networks (RBFNs). RBFNs are used to deal with the nonlinearity of the process. PRM is a promising linear robust regression method for tackling contaminated data, because it can efficiently eliminate the influence of outliers by appropriately chosen weighting scheme. Unlike other versions of robust PLS, NRPLS algorithm not only minimizes the adverse effect of outliers, but also characterizes the nonlinear feature. Simulation studies are performed for comparison with conventional nonlinear PLS methods. The NRPLS algorithm is also applied to cobalt hydrometallurgy extraction process. The results show superior performance compared to those methods of PLS, PRM and RBF-PLS.

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