Coping with complexity when predicting surface roughness in milling processes: hybrid incremental model with optimal parametrization

DOI10.1155/2017/7317254zbMath1380.93035DBLPjournals/complexity/BeruvidesCHSV17OpenAlexW2771974206WikidataQ59282289 ScholiaQ59282289MaRDI QIDQ1693809

Alberto Villalonga, Fernando Castaño, Rodolfo E. Haber, Gerardo Beruvides, Ramón Quiza

Publication date: 31 January 2018

Published in: Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2017/7317254

zbMATH Keywords

multilayer perceptronBayesian networkcomplexity of machining processeshybrid incremental modelingmeasurement of surface roughnesssimulated annealing for optimal parameters tuning

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Estimation and detection in stochastic control theory (93E10) Large-scale systems (93A15)

Cites Work

• Unnamed Item
• Nonparametric fuzzy regression -- \(k\)-NN and kernel smoothing techniques.
• Dynamic Model of the Machining Process on the Basis of Neural Networks: from Simulation to Real Time Application

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