DSpace Repository

Using spreadsheets as learning tools for computer simulation of neural networks

Show simple item record

dc.contributor.author Семеріков, Сергій Олексійович
dc.contributor.author Теплицький, Ілля Олександрович
dc.contributor.author Єчкало, Юлія Володимирівна
dc.contributor.author Маркова, Оксана Миколаївна
dc.contributor.author Соловйов, Володимир Миколайович
dc.contributor.author Ків, Арнольд Юхимович
dc.date.accessioned 2020-04-13T12:19:09Z
dc.date.available 2020-04-13T12:19:09Z
dc.date.issued 2020-03-26
dc.identifier.citation Semerikov S. Using spreadsheets as learning tools for computer simulation of neural networks [Electronic resource] / Serhiy Semerikov, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, Arnold Kiv // The International Conference on History, Theory and Methodology of Learning (ICHTML 2020). Kryvyi Rih, Ukraine, May 13-15, 2020 / Eds. : V. Hamaniuk, S. Semerikov, Y. Shramko // SHS Web of Conferences. – 2020. – Volume 75. – Article 04018. – Access mode : https://www.shs-conferences.org/articles/shsconf/abs/2020/03/shsconf_ichtml_2020_04018/shsconf_ichtml_2020_04018.html. – DOI : 10.1051/shsconf/20207504018 uk_UA
dc.identifier.issn 2261-2424
dc.identifier.other DOI : 10.1051/shsconf/20207504018
dc.identifier.uri https://www.shs-conferences.org/articles/shsconf/abs/2020/03/shsconf_ichtml_2020_04018/shsconf_ichtml_2020_04018.html
dc.identifier.uri http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/3720
dc.identifier.uri https://doi.org/10.1051/shsconf/20207504018
dc.description 1. H. Abelson, G.J. Sussman, J. Sussman, Structure and Interpretation of Computer Programs, 2nd edn. (MIT Press, Cambridge, 1996) 2. T.H. Abraham, (Physio)logical circuits: The intellectual origins of the McCulloch-Pitts neural networks. Journal of the History of the Behavioral Sciences. 38(1), 3–25 (2002). doi:10.1002/jhbs.1094 3. E. Anderson, The Species Problem in Iris. Annals of the Missouri Botanical Garden. 23(3), 457– 469+471–483+485–501+503–509 (1936). doi:10.2307/2394164. 4. E. Anderson, Plants, Man and Life (University of California Press, Boston, 1952) 5. E. Anderson, Bulletin of the American Iris Society. 59, 2–5 (1935) 6. E. Anderson, The Problem of Species in the Northern Blue Flags, Iris versicolor L. and Iris virginica L. Annals of the Missouri Botanical Garden. 15(3), 241–332 (1928). doi:10.2307/2394087 7. A.S. Ayed, Master thesis, Memorial University, 1997 8. J.J. Buergermeister, in Restructuring Training and Education through Technology, ed. by D.W. Dalton. 32nd Annual Conference of the Association for the Development of Computer-Based Instructional Systems, San Diego, California, October 29- November 1, 1990. (ADCIS International, Columbus, 1990), pp. 214–220 9. H. Chernoff, Journal of the American Statistical Association. 68(342), 361–368 (1973) 10. J.D. Cowan in Talking nets: An oral history of neural networks, ed. by J.A. Anderson, E. Rosenfeld (MIT Press, Cambridge, 1998), pp. 97–124 11. P. Cull, The mathematical biophysics of Nicolas Rashevsky. BioSystems. 88(3), 178–184 (2007). doi:10.1016/j.biosystems.2006.11.003 12. R.C. Eberhart, R.W. Dobbins, in Neural Network PC Tools: A Practical Guide, ed. by R.C. Eberhart, R.W. Dobbins (Academic Press, San Diego, 1990), pp. 9– 34 13. R.A. Fisher, The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics. 7(2), 179–188 (1936). doi:10.1111/j.1469- 1809.1936.tb02137.x 14. R.S. Freedman, R.P. Frail, F.T. Schneider, B. Schnitta, in Proceedings First International Conference on Artificial Intelligence Applications on Wall Street, Institute of Electrical and Electronics Engineers, New York, 9–11 Oct. 1991 15. T. Hegazy, A. Ayed, Neural Network Model for Parametric Cost Estimation of Highway Projects. Journal of Construction Engineering and Management. 124(3), 210–218 (1998). doi:10.1061/(ASCE)0733-9364(1998)124:3(210) 16. T.T. Hewett, Teaching Students to Model Neural Circuits and Neural Networks Using an Electronic Spreadsheet Simulator. Behavior Research Methods, Instruments, & Computers. 17(2), 339–344 (1985). doi:10.3758/BF03214406 17. T.T. Hewett, Using an Electronic Spreadsheet Simulator to Teach Neural Modeling of Visual Phenomena. (Drexel University, Philadelphia, 1985) 18. A.S. Householder, H.D. Landahl, Mathematical Biophysics of the Central Nervous System (Principia Press, Bloomington, 1945) 19. A.S. Householder, A neural mechanism for discrimination: II. Discrimination of weights. Bulletin of Mathematical Biophysics. 2(1), 1–13 (1940). doi:10.1007/BF02478027 20. A.S. Householder, A theory of steady-state activity in nerve-fiber networks I: Definitions and Preliminary Lemmas. Bulletin of Mathematical Biophysics. 3(2), 63–69 (1941). doi:10.1007/BF02478220 21. W. James, Psychology (Henry Holt and Company, New York, 1892) 22. W. James, The Principles of Psychology (Henry Holt and Company, New York, 1890) 23. S.J. Johnston, InfoWorld. 13(7), 14 (1991) 24. D.A. Kendrick, P.R. Mercado, H.M. Amman, Computational Economics (Princeton University Press, Princeton, 2006) 25. H.D. Landahl, W.S. McCulloch, W. Pitts, A statistical consequence of the logical calculus of nervous nets. Bulletin of Mathematical Biophysics. 5(4), 135–137 (1943). doi:10.1007/BF02478260 26. H.D. Landahl, R. Runge, Outline of a matrix calculus for neural nets. Bulletin of Mathematical Biophysics. 8(2), 75–81 (1946). doi:10.1007/BF02478464 27. H.D. Landahl, A matrix calculus for neural nets: II. Bulletin of Mathematical Biophysics. 9(2), 99–108 (1947). doi:10.1007/BF02478296 28. O. Markova, S. Semerikov, M. Popel, CoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. (CEUR Workshop Proceedings, 2018), http://ceur-ws.org/Vol-2104/paper_204.pdf. Accessed 30 Nov 2018 29. O.M. Markova, S.O. Semerikov, A.M. Striuk, H.M. Shalatska, P.P. Nechypurenko, V.V. Tron, Implementation of cloud service models in training of future information technology specialists. (CEUR Workshop Proceedings, 2019), http://ceurws.org/Vol-2433/paper34.pdf. Accessed 10 Sep 2019 30. W.C. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics. 5(4), 115–133 (1943). doi:10.1007/BF02478259 31. T.M. Mitchell, Key Ideas in Machine Learning. http://www.cs.cmu.edu/%7Etom/mlbook/keyIdeas.p df. Accessed 28 Jan 2019 32. O.S. Permiakova, S.O. Semerikov, Zastosuvannia neironnykh merezh u zadachakh prohnozuvannia (The use of neural networks in forecasting problems), in Materials of the International Scientific and Practical Conference “Young scientist of the XXI century”, KTU, Kryviy Rih, 17–18 November 2008 33. W. Pitts, W.S. McCulloch, How we know universals the perception of auditory and visual forms. Bulletin of Mathematical Biophysics. 9(3), 127–147 (1947). doi:10.1007/BF02478291 34. W. Pitts, A general theory of learning and conditioning: Part I. Psychometrika. 8(1), 1–18 (1943). doi:10.1007/BF02288680 35. W. Pitts, A general theory of learning and conditioning: Part II. Psychometrika. 8(2), 131–140 (1943). doi:10.1007/BF02288697 36. W. Pitts, Some observations on the simple neuron circuit. Bulletin of Mathematical Biophysics. 4(3), 121–129 (1942). doi:10.1007/BF02477942 37. W. Pitts, The linear theory of neuron networks: The dynamic problem. Bulletin of Mathematical Biophysics. 5(1), 23–31 (1943). doi:10.1007/BF02478116 38. W. Pitts, The linear theory of neuron networks: The static problem. Bulletin of Mathematical Biophysics. 4(4), 169–175 (1942). doi:10.1007/BF02478112 39. N. Rashevsky, Mathematical biophysics of abstraction and logical thinking. Bulletin of Mathematical Biophysics. 7(3), 133–148 (1945). doi:10.1007/BF02478314 40. N. Rashevsky, Outline of a physico-mathematical theory of excitation and inhibition. Protoplasma. 20(1), 42–56 (1933). doi:10.1007/BF02674811 41. N. Rashevsky, Some remarks on the boolean algebra of nervous nets in mathematical biophysics. Bulletin of Mathematical Biophysics. 7(4), 203–211 (1945). doi:10.1007/BF02478425 42. N. Rashevsky, The neural mechanism of logical thinking. Bulletin of Mathematical Biophysics. 8(1), 29–40 (1946). doi:10.1007/BF02478425 43. T.F. Rienzo, K.K. Athappilly, Introducing Artificial Neural Networks through a Spread-sheet Model. Decision Sciences Journal of Innovative Education. 10(4), 515–520 (2012). doi:10.1111/j.1540- 4609.2012.00363.x 44. M.A. Ruggiero, Cybernetic Trading Strategies: Developing a Profitable Trading System with Stateof-the-Art Technologies (John Wiley & Sons, New York, 1997) 45. M. Ruggiero, US Patent 5,241,620, 31 Aug 1993 46. K. Schwab, N. Davis, Shaping the Fourth Industrial Revolution (Portfolio Penguin, London, 2018) 47. S.O. Semerikov, I.O. Teplytskyi, Yu.V. Yechkalo, A.E. Kiv, Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. (CEUR Work-shop Proceedings, 2018), http://ceur-ws.org/Vol-2257/paper14.pdf. Accessed 21 Mar 2019 48. S.O. Semerikov, I.O. Teplytskyi, Metodyka uvedennia osnov Machine learning u shkilnomu kursi informatyky (Methods of introducing the basics of Machine learning in the school course of informatics), in Problems of informatization of the educational process in institutions of general secondary and higher education. Ukrainian scientific and practical conference, Kyiv, October 09, 2018. (Vyd-vo NPU imeni M. P. Drahomanova, Kyiv, 2018), pp. 18–20 49. A. Shimbel, A. Rapoport, A statistical approach to the theory of the central nervous system. Bulletin of Mathematical Biophysics. 10(2), 41–55 (1948). doi:10.1007/BF02478329 50. G.L. Stebbins, Edgar Anderson 1897-1969. (National Academy of Sciences, Washington, 1978) 51. G.J. Sussman, J. Wisdom, Structure and interpretation of classical mechanics, 2nd edn. (MIT Press, Cambridge, 2015) 52. I.O. Teplytskyi, O.I. Teplytskyi, A.P. Humeniuk, New computer technology. 6, 67–68 (2008) 53. I.O. Teplytskyi, Elementy kompiuternoho modeliuvannia (Elements of computer simulation), 2nd edn. (KSPU, Kryvyi Rih, 2010) 54. T. Wei, On matrices of neural nets. Bulletin of Mathematical Biophysics. 10(2), 63–67 (1948). doi:10.1007/BF02477433 55. P.J. Werbos, Maximizing long-term gas industry profits in two minutes in Lotus using neural network methods. Transactions on Systems Man and Cybernetics. 19(2), 315–333 (1989). doi:10.1109/21.31036 56. G. Young, On reinforcement and interference between stimuli. Bulletin of Mathematical Biophysics. 3(1), 5–12 (1941). doi:10.1007/BF02478102 57. T. Zaremba, in Neural Network PC Tools: A Practical Guide, ed. by R.C. Eberhart, R.W. Dobbins (Academic Press, San Diego, 1990), pp. 251–283
dc.description.abstract The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment with-out add-ins and macros. The article considers ways of building neural network models in cloud-based spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization. uk_UA
dc.language.iso en uk_UA
dc.publisher EDP Sciences uk_UA
dc.relation.ispartofseries SHS Web of Conferences;75
dc.subject computer simulation uk_UA
dc.subject neural networks uk_UA
dc.subject spreadsheets uk_UA
dc.subject neural computing uk_UA
dc.subject early network models uk_UA
dc.subject Anderson’s Iris uk_UA
dc.subject cloud-based learning tools uk_UA
dc.title Using spreadsheets as learning tools for computer simulation of neural networks uk_UA
dc.title.alternative EDP Sciences uk_UA
dc.type Article uk_UA


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics