Applying improved artificial neural network models to evaluate drilling rate index

The drilling rate index (DRI) is the most important input parameter of a commonly used performance prediction model for drilling and rock excavation. In this paper, the hybrid artificial neural network (ANN) with back propagation (BP) algorithm, simulated annealing algorithm (SAA), firefly algorithm...

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Veröffentlicht in:	Tunnelling and underground space technology Jg. 70; S. 114 - 124
Hauptverfasser:	Fattahi, Hadi, Bazdar, Habibollah
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 01.11.2017 Elsevier BV
Schlagworte:	Artificial neural network Artificial neural networks Back propagation Back propagation networks Compressive strength Computer simulation Correlation coefficients Drilling Drilling rate index Errors Firefly algorithm Heuristic methods Invasive weed optimization algorithm Mathematical models Neural networks Optimization algorithms Performance indices Performance prediction Scleroscope hardness Shuffled frog leaping algorithm Simulated annealing Simulated annealing algorithm Tensile strength Simulated annealing algorithm Drilling rate index Artificial neural network Invasive weed optimization algorithm Shuffled frog leaping algorithm Firefly algorithm
ISSN:	0886-7798, 1878-4364
Online-Zugang:	Volltext
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