title: Least square neural network model of the crude oil blending process.
abstract: In this paper, the recursive least square algorithm is designed for the big data learning of a feedforward neural network. The proposed method as the combination of the recursive least square and feedforward neural network obtains four advantages over the alone algorithms: it requires less number of regressors, it is fast, it has the learning ability, and it is more compact. Stability, convergence, boundedness of parameters, and local minimum avoidance of the proposed technique are guaranteed. The introduced strategy is applied for the modeling of the crude oil blending process.
authors.affiliation: Sección de Estudios de Posgrado e Investigación, ESIME Azcapotzalco, Instituto Politécnico Nacional, Av. de las Granjas no. 682, Col. Santa Catarina, México D.F., 02250, Mexico. Electronic address: jrubioa@ipn.mx.
authors.forename: José de Jesús
authors.initials: Jde J
authors.lastname: Rubio
mesh_terms: D000465:Algorithms; D016018:Least-Squares Analysis; D000069550:Machine Learning / Q000639:trends; D016571:Neural Networks, Computer; D010578:Petroleum / Q000032:analysis
chemical_list: D010578:Petroleum
keywords: Big data learning; Crude oil blending; Fast learning; Neural networks; Recursive least square; Stability