Sztuczne sieci neuronowe Kohonena jako narzędzie w taksonomii paleontologicznej - metodyka oraz zastosowanie na przykładzie późnokredowych belemnitów

• Autorzy: Remin Z.

Warianty tytułu

EN

Artificial Kohonen neural networks as a tool in paleontological taxonomy - an introduction and application to Late Cretaceous belemnites

• Języki publikacji: PL

Abstrakty

EN

Artificial neural networks (ANNs), the computer software or systems that are able to "learn" on the basis of previously collected input data sets are proposed here as a new useful tool in paleontological modeling. Initially ANNs were designed to imitate the structure and function of natural neural systems such as the human brain. They are commonly used in many natural researches such as physics, geophysics, chemistry, biology, applied ecology etc. Special emphasis is put on the Kohonen self-organizing mapping algorithm, used in unsupervised networks for ordination purposes. The application of ANNs for paleontology is exemplified by study of Late Cretaceous belemnites. The Kohonen networks objectively subdivided the belemnite material] ~ 750 specimens) into consistent groups that could be treated as monospecific. The possibility of transferring these results to the language of classical statistics is also presented. Further development and possibility of use of ANNs in various areas of paleontology, paleobiology and paleoecology is briefly discussed.

Słowa kluczowe

PL

paleontologia; sztuczna inteligencja; sieci neuronowe Kohonena; samoorganizujących się sieci Kohonena; klasyfikacja; belemnity; górna kreda

EN

paleontology; artificial intelligence; artificial neural networks; Kohonen neural networks; self-organizing map; classification; belemnites; Upper Cretaceous

• Wydawca: Państwowy Instytut Geologiczny - Państwowy Instytut Badawczy
• Czasopismo: Przegląd Geologiczny
• Rocznik: 2008
• Tom: Vol. 56, nr 1
• Strony: 58--66
• Opis fizyczny: bibliogr. 25 poz., rys., tab., wykr.

Twórcy

autor

Remin Z.

• Wydział Geologii, Uniwersytet Warszawski, ul. Żwirki i Wigury 93, 02-089 Warszawa,

Bibliografia

• BALDWIN J.L., OTTE D.N. & WHEATLEY C.L. 1989 - Computer emulation of human mental processes. Application of neural network simulators to problems in well log interpretation. Society of Petroleum Engineers of AIME, (Paper) SPE, OMEGA: 481-493.
• CHANG H.C., KOPASKA-MERKEL D.C. & CHEN H.C. 2002 - Identification of lithofacies using Kohonen self-organizing maps. Computers and Geosciences, 28: 223-229.
• CHON T.S., PARK Y.S., MOON K.H. & CHA E. 1996 - Patternizing communities by using an artificial neural network. Ecological Modelling, 90: 69-78.
• CHON T.S., PARK Y.S. & PARK J.H. 2000 - Determining temporal pattern of community dynamics by using unsupervised learning algorithms. Ecological Modelling, 132: 151-166.
• CHRISTENSEN W.K. 1995 - Belemnitella from the Upper Campanian and Lower Maastrichtian Chalk of Norfolk, England. Special Papers in Palaeontology, 51, 1-84.
• FAUSETT L. 1994 - Fundamentals of Neural Networks. Prentice Hall, New York.
• GUÉGAN J.F., LEK S. & OBERDORFF T. 1998 - Energy availability and habitat heterogeneity predict global riverine fish diversity. Nature, 391: 382-384.
• GISKE J., HUSE G. & FIKSEN O. 1998 - Modelling spatial dynamics of fish. Rev. Fish. Biol. Fish. 8: 57-91.
• HAYKIN S. 1994 - Neural Networks: A Comprehensive Foundation. MacMillan Publishing, New York.
• KAMINSKAS D. & MALMGREN B.A. 2004 - Comparison of pattern- recognition techniques for classification of Silurian rocks from Lithuania based on geochemical data. Norsk Geologisk Tidsskrift, 84: 117-124.
• KOHONEN T. 1982 - Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43: 59-69.
• KONGIEL R. 1962 - On belemnites from the Maastrichtian, Campanian and Santonian sediments in the Middle Vistula Valley (Central Poland). Pr. Muz. Ziemi, 5: 1-148.
• KRUK A. 2007 - Role of habitat degradation in determining fish distribution and abundance along the lowland Warta River, Poland. J. Appl. Ichthyol., 23: 9-18.
• MALMGREN B.A. & NORDLUND U. 1996 - Application of artificial neural networks to chemostratigraphy. Paleoceanogr., 11: 505-512.
• MALMGREN B.A. & NORDLUND U. 1997 - Application of artificial neural networks to paleoceanographic data. Palaeogeogr., Palaeoclimatol., Palaeoecol., 136: 359-373.
• MARMO R., AMODIO S. & CANTONI V. 2006 - Microfossils shape classification using a set of width values. Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06). Vol. 1, 20-24 Aug., 2006: 691-694.
• PATTERSON D. 1996 - Artificial Neural Networks. Prentice Hall, Singapore.
• RECKNAGEL F., FRENCH M., HARKONEN P. & YABUNAKA K.I. 1997 - Artificial neural network approach for modelling and prediction of algal blooms. Ecological Modelling, 96: 11-28.
• REMIN Z. 2007 - Analiza paleontologiczna i znaczenie stratygraficzne belemnitów górnego kampanu i dolnego mastrychtu profilu doliny oerodkowej Wisły. Arch. WG UW, Warszawa.
• ROGERS S.J., FANG J.H., KARR C.L. & STANLEY D.A. 1992 - Determination of lithology from well logs using a neural network. Am. Assoc. Pet. Geol. Bull., 76: 731-739.
• SCHULZ M.G. 1979 - Morphometrisch-variationsstatistische Untersuchungen zur Phylogenie der Belemniten-Gattung Belemnella im Untermaastricht NW-Europas. Geol. Jahr., A47: 3-157.
• SCARDI M. 1996 - Artificial neural networks as empirical models for estimating phytoplankton production. Marine Ecol. Progr. Series 139: 289-299.
• SEGINER I., BOULARD T. & BAILEY B.J. 1994 - Neural network models of the greenhouse climate. J. Agric. Eng. Res. 59: 203-216.
• StatSoft 2001 - Statistica Neural Networks PL. (I) Wprowadzenie do sieci neuronowych; (II) Poradnik użytkownika; (III) Przewodnik problemowy. StatSoft Polska Sp. z.o.o., Kraków.
• StatSoft 2006 - Elektroniczny Podręcznik Statystyki PL. Kraków. WEB: http://www.statsoft.pl/textbook/stathome.html.