LEADER 03298nam 22005415 450 001 9910299565403321 005 20200630172648.0 010 $a3-319-71264-0 024 7 $a10.1007/978-3-319-71264-2 035 $a(CKB)4340000000223574 035 $a(DE-He213)978-3-319-71264-2 035 $a(MiAaPQ)EBC5152979 035 $a(PPN)221249192 035 $a(EXLCZ)994340000000223574 100 $a20171120d2018 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEnsembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction /$fby Jesus Soto, Patricia Melin, Oscar Castillo 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (VIII, 97 p. 101 illus., 73 illus. in color.) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3704 311 $a3-319-71263-2 320 $aIncludes bibliographical references and index. 330 $aThis book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. This book describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series prediction. Interval type-2 and type-1 fuzzy systems are used to integrate the outputs of the Ensemble of Interval Type-2 Fuzzy Neural Network models. Genetic Algorithms and Particle Swarm Optimization are the Bio-Inspired algorithms used for the optimization of the fuzzy response integrators. The Mackey-Glass, Mexican Stock Exchange, Dow Jones and NASDAQ time series are used to test of performance of the proposed method. Prediction errors are evaluated by the following metrics: Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Percentage Error and Mean Absolute Percentage Error. The proposed prediction model outperforms state of the art methods in predicting the particular time series considered in this work.  . 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3704 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a519.55 700 $aSoto$b Jesus$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063290 702 $aMelin$b Patricia$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aCastillo$b Oscar$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299565403321 996 $aEnsembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction$92531484 997 $aUNINA