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Teoria szarych systemów – nowa metodologia analizy i oceny złożonych systemów. Przegląd możliwości

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Warianty tytułu
EN
Grey systems theory – new methodology of analysis and evaluation of complex systems
Języki publikacji
PL
Abstrakty
PL
Większość złożonych systemów, jakie rozpatruje się w nauce, technologii i gospodarce, ma niepełne i niepewne informacje o swej strukturze i zachowaniu. Do grona metod, jakimi można je analizować i oceniać (probabilistyka, zbiory rozmyte i zgrubne), warto dołączyć teorię sza-rych systemów (GST), bo nie wymaga ona wielu założeń o wielkości i rozkładzie próbki tkwią-cych we wspomnianych metodach, a upoważniająca do zastosowań GST minimalna liczba danych n ≥ 4. Za jej pomocą można prognozować przyszłe zachowanie systemu, oceniać współzależność wektorów obserwacji oraz oceniać efektywność reakcji na możliwe sytuacje i podejmować optymalne decyzje w tym względzie, a także je grupować i badać skupienie.
EN
Most of the complex systems we are considering in science, technology, social care and economy have uncertain and incomplete information concerning the system behaviour, its structure, boundaries, interaction with environment, etc. In order to omit these troubles and information lack, we use sometimes statistics and probabilistic approach, fuzzy and rough sets methodology. As it is shown in this review paper, we can use with much success new methodology – Grey Systems Theory (GST), which do not need any assumption concern-ing the distribution of sample, and high amount of data, because minimal number of obser-vations for GST use is only n ≥ 4. Using GST one can forecast the future behaviour of complex system, evaluate interdependence of its observation vectors (cause and effect), and evaluate optimal decisions possible to undertake in a given situations of decision making, as well as clustering of the similar systems.
Rocznik
Tom
Strony
9--20
Opis fizyczny
Bibliogr. 60 poz.
Twórcy
autor
  • Instytut Mechaniki Stosowanej, Wydział Budowy Maszyn i Zarządzania Politechniki Poznańskiej
Bibliografia
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  • [29] Liu J., Qiao J-Z., A grey rough set model for evaluation and selection of software cost estimation methods, Grey Systems; Theory and Application, 2014, Vol. 4, No. 1, s. 3-12.
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  • [32] Liu S.F., Lin Y., Grey Systems – Theory and Applications, Springer-Verlag, Berlin 2010, s. 379.
  • [33] Liu S.F., Zhu Y.D., Grey-Econometrics Combined Model, The Journal of Grey System, 1996, Vol. 8, No. 1, s. 103-110
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  • [35] Liu S-F., Forrest J., Yingjie Y., Advances in grey systems research, The Journal of Grey Systems, 2013, Vol. 25, No. 2, s. 1-18.
  • [36] Liu X.Q., Wang Z.M., Grey Econometric Models and Applications, Yellow River Press, Jinan, 1996.
  • [37] Luo M.F., Fault detection, diagnosis and prognosis using GST, PhD Engineering, Monash University Australia, 1994.
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  • [39] Mao M., Chirwa E.C., Application of grey model GM(1,1) to vehicle fatality risk estimation, Technological Forecasting and Social Change, 2006, No 73, s. 588-605.
  • [40] Pan G., Wang S., Forecast accident of deep foundation pit by using grey systems, Journal of Tongji University, 1999, 03.
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  • [42] Scarlet E., Delcea C., Complete Analysis of bankruptcy syndrome using grey systems theory, Grey systems Theory and Applications, 2011, Vol. 1, No. 1, s. 19-32.
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  • [46] Wang Z.X., Pei L.L., System thinking – based grey model for sustainability evaluation of urban tourism, Kybernetes, 2014, Vol. 43, No. 3 / 4, s. 462-479.
  • [47] Wen K.L., Hsieh W.F., Optimal teacher evaluation based on cardinal grey relational grade, J. Chien-Kuo Institute of Technology, April 2003, s. 29-38.
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  • [49] Wu H., Chen F., The application of GST to exchange rate prediction in post-crisis era, International Journal of Innovative Management, Information & Production, 2011, Vol. 2, No 2, s. 83-89.
  • [50] Wu W.Y., Chen S.P., A prediction method using the grey model GMC(1,n) combined with the grey relational analysis: a case study on Internet access population forecast, Applied Mathematics and Computation, 2005, No. 169, s. 198-217.
  • [51] Yang J-M., Shi J-J., Xiong S-F., Application of grey systems theory in the prediction of mine safety accident, Mining Research and Development, 2004, 01.
  • [52] Yang Y., Wang S.W., Hao N.L., Shen X.B., Qi X.H., Online noise source identification based on power spectrum estimation and grey relational analysis, Applied Acoustics, 2009, Vol. 70, No. 3, s. 493-497.
  • [53] Yi D.S., Grey models and prediction of human talents, Systems Engineering, 1987, Vol. 5, No.1, s. 36-43.
  • [54] Yuan Ch., Guo B., Liu H., Assessment and classification of China’s provincial regional innovation system based on fixed weight clustering, Grey Systems Theory and Applications, 2013, Vol. 3, No. 3, s. 316-337.
  • [55] Zadeh L.A., Fuzzy sets, Information and Control, 1965, Vol. 8, s. 338-353.
  • [56] Zhang H., Song W., Hazard source identification of mined-out area based on grey systems theory, red. Zhu et al., ICICA 2010, LNCS 6377, 2010, s. 493-500.
  • [57] Zheng B., Grey Systems Modeling Software 6, Institutes of Grey Systems, Nanging University of Aeronautics and Astronautics, China, 2012.
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  • [59] Zhu C.H., Li N.P., Re D., Guan J., Uncertainty in indoor air quality and grey systems method, Building and Environment, 2007, Vol. 42, No. 4, s. 1711-1717.
  • [60] Żółtowski B., Consideration in diagnostics of the grey systems theory, Journal of Polish CIMAC, 2011, Vol. 6, No. 2, s. 191-200.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b63f4201-1986-4956-8c55-34a5c1f9c9bb
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