Krichevsky М.L.Monograph:
ISBN: 978-5-91292-221-3
Format: 60х84/16
Amount: 500
Published: 06.03.2018
Publisher: Creative Economy Publishers


Keywords: fuzzy logic, neural networks, quantitative methods in management, time series in management


Share:

Abstract:

We present solutions to the most common practical tasks in management, in particular, in staff management, financial management and forecasting time series. All tasks were solved through application of tools from the field of artificial intelligence, including neural networks, fuzzy logic and neural-fuzzy systems. The methodology has created the basis for the transition to quantitative management. The book may be useful to analysts, teachers, postgraduates, students in management, who use the intelligent technologies in their work.

---

References:
1. Curwin J. , Slater R. Quantitative Methods for Business Decisions. London, Cengage Learning EMEA. 2008. – 790 p.
2. Jolliffe I. Principal Component Analysis. New York: Springer. – 2002. – 518 p.
3. R. Bro, A. K. Smilde. Principal component analysis. Anal. Methods, 2014, 6, 2812 – 2831.
4. ec.europa.eu/eurostat (data obrascheniya 24.01.17)
5. Kaufman L., Rousseeuw P.J. Finding Groups in Data: An Introduction to Cluster Analysis. – New York. Wiley&Sons. 2005. – 336 p.
6. Everitt B.S., Landau S., Leese M. et al. Cluster Analysis. – New York. Wiley&Sons. 2011. –346 r.
7. Sarstedt M., Mooi E. A Concise Guide to Market Research. Springer-Verlag, Berlin, Heidelberg. 2014. – 328 r
8. Khaykin S. Neyronnye seti: polnyy kurs. – M.: Izd. dom Vilyams, 2006. – 1104s.
9. McNelis P. D. Neural networks in finance: gaining predictive edge in the market. Elsevier Academic Press, 2005. – 261 r.
10. Kriesel D. A Brief Introduction to. Neural Networks. Download location. http://www.dkriesel.com/en/science/neural_networks.
11. Minskiy M., Paypert S. Perseptrony. – M.: Mir, 1971. – 365 s.
12. Rumelhart D.E., Hinton G.E., Williams R.J. Learning Internal Representations by Error Propagation. // Parallel Distributed Processing. Eds. Rumerhart D.E., McClelland J.L. Vol. 1, chap. 8, Cambridge: MIT Press, 1986.
13 Vapnik V.N. Statistical Learning Theory. – NY: J.Wiley, 1998. – 740 p.
14. G. E. Hinton, R. R. Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks. Science, 2006, № 313, R. 504–507.
15. Gudfellou Ya., Bendzhio I., Kurvill A.. Glubokoe obuchenie. – M.: DMK-Press, 2017. – 652 s.
16. Investitsionnaya deyatelnosty v Rossii: usloviya, faktory, tendentsii. – M.: Rosstat, 2016. http://www.gks.ru/wps/wcm/connect/ rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1140095471812.
17. www.basegroup.ru – Tekhnologii analiza dannyh.
18. Kecman V. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. The MIT Press, Cambridge, 2001. – 542 p.
19. L. Zade. Ponyatie lingvisticheskoy peremennoy i ego primenenie k prinyatiyu priblizhennyh resheniy. – M.: Mir, 1976. – 166 s.
20. Bojadziev G., Bojadziev V. Fuzzy Logic for Business, Finance and Management. World Scientific, Singapore, 2007. – 232 p.
21. Pegat A. Nechetkoe modelirovanie i upravlenie.  M.: Binom. Laboratoriya znaniy, 2009. – 798 s.
22. Kecman V. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. MIT Press, Cambridge, MA, 2001 – 542 p.
23. Leonenkov A.V. Nechetkoe modelirovanie v srede MATLAB i fuzzyTECH.  SPb: BKhV-Peterburg, 2003. – 736 s.
24. www.mathworks.com – Opisanie MATLAB R2012b
25. Dmitrieva S.V., Krichevskiy M.L., Martynova Yu.A. Otsenka kachestva pri podbore personala // Aktualnye problemy ekonomiki i upravleniya.  2016.  № 2(10).  S. 95 100.
26. Krichevskiy M.L., Kozlova Yu.A. Otsenka privlekatelnosti innovatsionnyh produktov // Upravlenie riskom.  2015.  № 1.  S. 58 64.
27. Jang J-S. R., C-T Sun, E. Mizutani (1997). Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence.Prentice-Hall,rr: 640.
28. Dursun, M., Karsak, E. E. (2010). A fuzzy MCDM approach for personnel selection. Expert Systems with Applications, 37(6): 4324 4330.
29. Rouyendegh B.D., T.E. Erkan (2013). An application of the fuzzy ELECTRE method for academic staff selection. Human Factors and Ergonomics in Manufacturing and Service Industries, 23(2): 107 115.
30. G. Kirchgdssner, J.Wolter. Introduction to Modern Time Series Analysis. Springer-Verlag Berlin Heidelberg, 2007. – 276 p.
31. Boks Dzh., Dzhenkins G. Analiz vremennyh ryadov: prognoz i upravlenie. Vyp. 1. – M.: Mir, 1974. – 406 s.
32. Brown, R. G. Smoothing, forecasting and prediction of discrete time series. Englewood Cliffs, NJ7 Prentice-Hall, 1963. – 468 r.
33. Gardner Jr., E. S., Exponential smoothing: The state of the art – Part II. 2005. http://www.bauer.uh.edu/gardner/docs/pdf/Exponential-Smoothing.pdf.
34. Gardner Jr., E. S. Exponential smoothing: The state of the art. Journal of Forecasting, 1985, 4, R. 1–38.
35. Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 2002, 18, R. 439–454.
36. Guide to Seasonal Adjustment with X-12-ARIMA, https://www. google.ru/search?sourceid=navclient&hl, 2007.
37. Chatfield C. Time-series forecasting. Chapman & Hall/CRC, London, 2000. – 266 r.
38. Engle R.F. Autoregressive conditional Heteroscedasticity with estimates of the variance of the United Kingdom inflation. Econometrica, 1982, V. 50, R. 987–1007.
39. Dzhenkins G., Vatts D. Spektralnyy analiz i ego prilozheniya. Vyp. 1. – M.: Mir, 1971. – 317 s.
40. Grendzher K., Khatanaka M. Spektralnyy analiz vremennyh ryadov v ekonomike. – M.: Statistika,1972. – 312 s.
41. Malla S. Veyvlety v obrabotke signalov. – M.: Mir, 2005. – 671 s.
42. Percival D., Walden A. Wavelet Methods for Time Series Analysis, Cambridge University Press, 2000. – 594 r.
43. Kolmogorov A.N. Interpolirovanie i ekstrapolirovanie statsionarnyh sluchaynyh posledovatelnostey // Izv. AN SSSR, seriya matematicheskaya. – 1941. – T. 5. – № 1. – S. 3–14.
44. Wiener N. Extrapolation, interpolation and smoothing of stationary time series, N. Y., 2013 (Reprint of 1949 Edition). – 174 r.
45. Tsay R. S. Analysis of financial time series. John Wiley & Sons, 2002. – 457 r.
46. Song Q., Chissom B.S. Fuzzy time series and its models, Fuzzy Sets and Systems, 1993, 54, R. 269–277.
47. Song Q., Chissom B.S. Forecasting enrollments with fuzzy time series – part 1, Fuzzy Sets and Systems ,1993, 54, R. 1–9.
48. Song Q., Chissom B.S. Forecasting enrollments with fuzzy time series – part 2, Fuzzy Sets and Systems, 1994, 62, R. 1–8.
49. Chen S-M. Forecasting enrollments based on fuzzy time series, Fuzzy Sets and Systems, 1996, 81, R. 311–319.
50. Huarng K. Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems, 2001, 123, R. 369–386.
51. Krichevskiy M.L. Prognozirovanie nechetkikh vremennyh ryadov // Aktualnye problemy ekonomiki sovremennoy Rossii». –2013. – Vyp. 9. – S. 162–166.