@article {Edwards:1 April 2000:0371-7844:23,
	author = "Edwards, D.J.",
	author = "Griffiths, I.J.",
	title = "Artificial intelligence approach to calculation of hydraulic excavator cycle time and output",
	journal = "Mining Technology: Transactions of the Institute of Mining and Metallurgy, Section A",
	volume = "109",
	year = "1 April 2000",
	abstract = "Accurate prediction of the cycle time and output of tracked hydraulic excavators is notoriously difficult, not least because such data are freely available to the mining practitioner from only a limited number of plant manufacturers. Previous research attempted to rectify this problem through the development of ESTIVATE. ESTIVATE utilized a multiple regression (MR) equation to predict machine cycle time and subsequently, on the basis of this, to estimate machine output and excavation costs. However, with a coefficient of determination (<I>R</I><SUP>2</SUP>) of 0.88 and a mean absolute percentage error (<I>MAPE</I>) of 20&#037;, the MR equation failed to provide an adequately robust predictor of machine cycle time. Improvement to ESTIVATE's predictive capacity was sought through the use of a feed-forward artificial neural network with back-propagation training. With a sum square error of 0.194 and a <I>MAPE</I> of 7&#037; (that is, a 14&#037; reduction on the equivalent MR equation) the feed-forward network provides a significant improvement over the MR equation. An aim in future work will be to expand the capability of the model to include long-reach machines.",
	pages = "23-29(7)",
	url = "http://www.ingentaconnect.com/content/maney/mint/2000/00000109/00000001/art00003"
}