A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance

DOI10.1016/j.cam.2017.02.031zbMath1422.74082OpenAlexW2591797778MaRDI QIDQ1676019

Peng Zhang

Publication date: 3 November 2017

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2017.02.031

zbMATH Keywords

particle swarm optimization (PSO)support vector machines (SVMs)multivariate adaptive regression splines (MARS)artificial neural networks (ANNs)centerline segregation predictioncontinuous cast steel slabs

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Crystals in solids (74N05)

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Uses Software

• LIBSVM
• earth
• ABC
• hydroPSO

Cites Work

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• Multivariate adaptive regression splines
• A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
• Cross-Validation of Regression Models
• Particle Swarm Optimization
• Support Vector Machines
• A new data mining methodology applied to the modelling of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women

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