000 03620nam a22004815i 4500
001 978-3-642-25786-5
003 DE-He213
005 20140220083306.0
007 cr nn 008mamaa
008 120125s2012 gw | s |||| 0|eng d
020 _a9783642257865
_9978-3-642-25786-5
024 7 _a10.1007/978-3-642-25786-5
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aRotshtein, Alexander P.
_eauthor.
245 1 0 _aFuzzy Evidence in Identification, Forecasting and Diagnosis
_h[electronic resource] /
_cby Alexander P. Rotshtein, Hanna B. Rakytyanska.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2012.
300 _aXIV, 314p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Fuzziness and Soft Computing,
_x1434-9922 ;
_v275
505 0 _aPreface -- Fundamentals of intellectual technologies -- Direct inference based on fuzzy rules -- Fuzzy rules tuning for direct inference -- Fuzzy rules extraction from experimental data -- Inverse inference based on fuzzy relational equations -- Inverse inference with fuzzy relations tuning -- Inverse inference based on fuzzy rules -- Fuzzy relations extraction from experimental data -- Applied fuzzy systems.
520 _aThe purpose of this book is to present a methodology for designing and tuning fuzzy expert systems in order to identify nonlinear objects; that is, to build input-output models using expert and experimental information. The results of these identifications are used for direct and inverse fuzzy evidence in forecasting and diagnosis problem solving. The book is organised as follows: Chapter 1 presents the basic knowledge about fuzzy sets, genetic algorithms and neural nets necessary for a clear understanding of the rest of this book. Chapter 2 analyzes direct fuzzy inference based on fuzzy if-then rules. Chapter 3 is devoted to the tuning of fuzzy rules for direct inference using genetic algorithms and neural nets. Chapter 4 presents models and algorithms for extracting fuzzy rules from experimental data. Chapter 5 describes a method for solving fuzzy logic equations necessary for the inverse fuzzy inference in diagnostic systems. Chapters 6 and 7 are devoted to inverse fuzzy inference based on fuzzy relations and fuzzy rules. Chapter 8 presents a method for extracting fuzzy relations from data. All the algorithms presented in Chapters 2-8 are validated by computer experiments and illustrated by solving medical and technical forecasting and diagnosis problems. Finally, Chapter 9 includes applications of the proposed methodology in dynamic and inventory control systems, prediction of results of football games, decision making in road accident investigations, project management and reliability analysis.     
650 0 _aEngineering.
650 0 _aComputer simulation.
650 0 _aOptical pattern recognition.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aSimulation and Modeling.
650 2 4 _aPattern Recognition.
700 1 _aRakytyanska, Hanna B.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642257858
830 0 _aStudies in Fuzziness and Soft Computing,
_x1434-9922 ;
_v275
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-25786-5
912 _aZDB-2-ENG
999 _c102488
_d102488