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Application of Kalman learning algorithm multilayer neural network to estimates of ore grades
Luleå tekniska universitet.
Luleå tekniska universitet.
1998 (English)In: International Journal of Mining, Reclamation and Environment, ISSN 1748-0930, E-ISSN 1748-0949, Vol. 12, no 1, p. 19-27Article in journal (Refereed) Published
Abstract [en]

Lead and zinc grades are estimated along boreholes in an ore body based on geophysical logging data by using a Kalman learning algorithm, which a variety of a backpropagation neural network. Data are acquired from seven boreholes in the Zinkgruvan Mine. Five of the boreholes are used for training the network and two boreholes are used for testing the successfulness in employing the network results. The principal idea of the Kalman learning algorithm is discussed. The minimum error rates, average prediction errors and optimum number of training epochs of lead and zinc is presented. The Kalman learning algorithm is more effective than the conventiaonal backpropagation algorithm in predicting ore grades

Place, publisher, year, edition, pages
1998. Vol. 12, no 1, p. 19-27
National Category
Other Civil Engineering
Research subject
Mining and Rock Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-7899DOI: 10.1080/09208119808944017Local ID: 652a4a40-fbf2-11dc-a946-000ea68e967bOAI: oai:DiVA.org:ltu-7899DiVA, id: diva2:980789
Note
Godkänd; 1998; 20080327 (cira)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-21Bibliographically approved

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