LEADER 04303nam 22006495 450 001 9910299851303321 005 20251116140217.0 010 $a1-4614-3442-4 024 7 $a10.1007/978-1-4614-3442-9 035 $a(CKB)3710000000311454 035 $a(EBL)1964797 035 $a(OCoLC)897810228 035 $a(SSID)ssj0001408120 035 $a(PQKBManifestationID)11787287 035 $a(PQKBTitleCode)TC0001408120 035 $a(PQKBWorkID)11346120 035 $a(PQKB)11111320 035 $a(DE-He213)978-1-4614-3442-9 035 $a(MiAaPQ)EBC1964797 035 $a(PPN)183146891 035 $a(EXLCZ)993710000000311454 100 $a20141203d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFrontiers of higher order fuzzy sets /$fedited by Alireza Sadeghian, Hooman Tahayori 205 $a1st ed. 2015. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2015. 215 $a1 online resource (266 p.) 300 $aDescription based upon print version of record. 311 08$a1-4614-3441-6 320 $aIncludes bibliographical references and index. 327 $aA New Fuzzy Disjointing Difference Operator to Calculate Union and Intersection of Type-2 Fuzzy Sets -- Robustness of Higher Order Fuzzy Sets -- Fuzzy Sets of Higher Type and Higher Order in Fuzzy Modeling -- Recent Advances in Fuzzy System Modeling -- On the use of participatory genetic fuzzy system approach to develop fuzzy models -- Fuzzy Modelling of Economic Institutional Rules -- Modeling the uncertainty of a set of graphs using higher order fuzzy sets -- Time-Series Forecasting via Complex Fuzzy Logic -- Multi-Subject Type-2 Linguistic Summaries of Relational Databases -- Bio-Inspired Optimization of Interval Type-2 Fuzzy Controller Design -- Image Processing and Pattern Recognition with Interval Type-2 Fuzzy Inference Systems -- Big Data Analytic via Soft Computing Paradigms. 330 $aFrontiers of Higher Order Fuzzy Sets, strives to improve the theoretical aspects of general and Interval Type-2 fuzzy sets and provides a unified representation theorem for higher order fuzzy sets. Moreover, the book elaborates on the concept of gradual elements and their integration with the higher order fuzzy sets. This book also introduces new frameworks for information granulation based on general T2FSs, IT2FSs, Gradual elements, Shadowed sets and rough sets. In particular, the properties and characteristics of the new proposed frameworks are studied. Such new frameworks are shown to be more capable to be exploited in real applications. Higher order fuzzy sets that are the result of the integration of general T2FSs, IT2FSs, gradual elements, shadowed sets and rough sets will be shown to be suitable to be applied in the fields of bioinformatics, business, management, ambient intelligence, medicine, cloud computing and smart grids. Presents new variations of fuzzy set frameworks and new areas of applicability of fuzzy theory Provides unified method for representing higher order fuzzy sets Discusses the role of gradual elements in fuzzy sets. 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aData structures (Computer science) 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aData Structures and Information Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/I15009 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aData structures (Computer science) 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aData Structures and Information Theory. 676 $a005.74 676 $a006.3 676 $a620 702 $aSadeghian$b Alireza$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTahayori$b Hooman$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910299851303321 996 $aFrontiers of Higher Order Fuzzy Sets$91465818 997 $aUNINA