Frequently, the number of input variables (features) involved in a problem becomes too large to be easily handled by conventional machine-learning models. This paper introduces a combined strategy that uses a real-coded genetic algorithm to find the optimal scaling (RCGA-S) or scaling + projection (RCGA-SP) factors that minimize the Delta Test criterion for variable selection when being applied...

Multi-input multi-output (MIMO) dynamic matrix control (DMC) technique is applied to control steam temperatures in a large-scale ultrasupercritical once-through boiler–turbine system. Specifically, four output variables (i.e., outlet temperatures of platen superheater, finish superheater, primary reheater, and finish reheater) are controlled using four input variables (i.e., two spray valves, b...

In this study, an unknown input observer (UIO) is developed in explicit form to estimate unmeasurable states and inputs (UIs) for nonlinear implicit systems represented by the discrete-time Takagi-Sugeno (DTSIS) case of premise variables. The method employed based on singular value decomposition (SVD) augmenting state vector, which formed partly system UIs. convergence augmented estimation erro...

In supervised machine learning, variable ranking aims at sorting the input variables according to their relevance w.r.t. an output variable. In this paper, we propose a new relevance criterion for variable ranking in a regression problem with a large number of variables. This criterion comes from a discretization of both input and output variables, derived as an extension of a Bayesian non para...