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020 _a9783319351629
_9978-3-319-35162-9
024 7 _a10.1007/978-3-319-35162-9
_2doi
050 4 _aQ342
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_2bicssc
072 7 _aTEC009000
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_223
100 1 _aZgurovsky, Mikhail Z.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_955561
245 1 4 _aThe Fundamentals of Computational Intelligence: System Approach
_h[electronic resource] /
_cby Mikhail Z. Zgurovsky, Yuriy P. Zaychenko.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXX, 375 p. 143 illus., 70 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v652
505 0 _aNeural Networks -- . Neural Networks with Feedback and Self-organization Introduction -- Fuzzy Inference Systems and Fuzzy Neural Networks -- Application of Fuzzy Logic Systems and Fuzzy Neural Networks in Forecasting Problems in Macroeconomics and Finance -- Fuzzy Neural Networks in Classification Problems -- Inductive Modeling Method (gmdh) in Problems of Intellectual Data Analysis and Forecasting -- The Cluster Analysis in Intellectual Systems -- Genetic Algorithms and Evolutionary Programing -- Problem of Fuzzy Portfolio optimization Under Uncertainty And Its Solution With Application of Computational Intelligence Methods.
520 _aThis monograph is dedicated to the systematic presentation of main trends, technologies and methods of computational intelligence (CI). The book pays big attention to novel important CI technology- fuzzy logic (FL) systems and fuzzy neural networks (FNN). Different FNN including new class of FNN- cascade neo-fuzzy neural networks are considered and their training algorithms are described and analyzed. The applications of FNN to the forecast in macroeconomics and at stock markets are examined. The book presents the problem of portfolio optimization under uncertainty, the novel theory of fuzzy portfolio optimization free of drawbacks of classical model of Markovitz as well as an application for portfolios optimization at Ukrainian, Russian and American stock exchanges. The book also presents the problem of corporations bankruptcy risk forecasting under incomplete and fuzzy information, as well as new methods based on fuzzy sets theory and fuzzy neural networks and results of their application for bankruptcy risk forecasting are presented and compared with Altman method. This monograph also focuses on an inductive modeling method of self-organization – the so-called Group Method of Data Handling (GMDH) which enables to construct the structure of forecasting models almost automatically. The results of experimental investigations of GMDH for forecasting at stock exchanges are presented. The final chapters are devoted to theory and applications of evolutionary modeling (EM) and genetic algorithms. The distinguishing feature of this monograph is a great number of practical examples of CI technologies and methods application for solution of real problems in technology, economy and financial sphere, in particular forecasting, classification, pattern recognition, portfolio optimization, bankruptcy risk prediction under uncertainty which were developed by authors and published in this book for the first time. All CI methods and algorithms are presented from the general system approach and analysis of their properties, advantages and drawbacks that enables practitioners to choose the most adequate method for their own problems solution. .
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aZaychenko, Yuriy P.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_955562
710 2 _aSpringerLink (Online service)
_955563
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319351605
776 0 8 _iPrinted edition:
_z9783319351612
776 0 8 _iPrinted edition:
_z9783319817392
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v652
_955564
856 4 0 _uhttps://doi.org/10.1007/978-3-319-35162-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79580
_d79580