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      Neural Networks In Mining Sciences – General Overview And Some Representative Examples

      Archives of Mining Sciences
      Walter de Gruyter GmbH

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The many difficult problems that must now be addressed in mining sciences make us search for ever newer and more efficient computer tools that can be used to solve those problems. Among the numerous tools of this type, there are neural networks presented in this article – which, although not yet widely used in mining sciences, are certainly worth consideration. Neural networks are a technique which belongs to so called artificial intelligence, and originates from the attempts to model the structure and functioning of biological nervous systems. Initially constructed and tested exclusively out of scientific curiosity, as computer models of parts of the human brain, neural networks have become a surprisingly effective calculation tool in many areas: in technology, medicine, economics, and even social sciences. Unfortunately, they are relatively rarely used in mining sciences and mining technology. The article is intended to convince the readers that neural networks can be very useful also in mining sciences. It contains information how modern neural networks are built, how they operate and how one can use them. The preliminary discussion presented in this paper can help the reader gain an opinion whether this is a tool with handy properties, useful for him, and what it might come in useful for.

          Of course, the brief introduction to neural networks contained in this paper will not be enough for the readers who get convinced by the arguments contained here, and want to use neural networks. They will still need a considerable portion of detailed knowledge so that they can begin to independently create and build such networks, and use them in practice. However, an interested reader who decides to try out the capabilities of neural networks will also find here links to references that will allow him to start exploration of neural networks fast, and then work with this handy tool efficiently. This will be easy, because there are currently quite a few ready-made computer programs, easily available, which allow their user to quickly and effortlessly create artificial neural networks, run them, train and use in practice.

          The key issue is the question how to use these networks in mining sciences. The fact that this is possible and desirable is shown by convincing examples included in the second part of this study. From the very rich literature on the various applications of neural networks, we have selected several works that show how and what neural networks are used in the mining industry, and what has been achieved thanks to their use. The review of applications will continue in the next article, filed already for publication in the journal „Archives of Mining Sciences“. Only studying these two articles will provide sufficient knowledge for initial guidance in the area of issues under consideration here.

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          Capability of self-organizing map neural network in geophysical log data classification: Case study from the CCSD-MH

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            Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information

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              Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network

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                Author and article information

                Journal
                Archives of Mining Sciences
                Walter de Gruyter GmbH
                1689-0469
                December 1 2015
                December 1 2015
                : 60
                : 4
                : 971-984
                Article
                10.1515/amsc-2015-0064
                c8a374bd-46c8-43aa-b416-d60f508465af
                © 2015

                http://creativecommons.org/licenses/by-nc-nd/3.0

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