000824759 000__ 03049cam\a2200505Ii\4500 000824759 001__ 824759 000824759 005__ 20230306144138.0 000824759 006__ m\\\\\o\\d\\\\\\\\ 000824759 007__ cr\cn\nnnunnun 000824759 008__ 171127s2018\\\\sz\a\\\\o\\\\\001\0\eng\d 000824759 019__ $$a1013173210$$a1017844091$$a1032279816 000824759 020__ $$a9783319712642$$q(electronic book) 000824759 020__ $$a3319712640$$q(electronic book) 000824759 020__ $$z9783319712635 000824759 020__ $$z3319712632 000824759 0247_ $$a10.1007/978-3-319-71264-2$$2doi 000824759 035__ $$aSP(OCoLC)on1012939254 000824759 035__ $$aSP(OCoLC)1012939254$$z(OCoLC)1013173210$$z(OCoLC)1017844091$$z(OCoLC)1032279816 000824759 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dN$T$$dYDX$$dGW5XE$$dAZU$$dOCLCF 000824759 049__ $$aISEA 000824759 050_4 $$aQA280 000824759 08204 $$a519.5/5$$223 000824759 1001_ $$aSoto, Jesus,$$eauthor. 000824759 24510 $$aEnsembles of type 2 fuzzy neural models and their optimization with bio-inspired algorithms for time series prediction /$$cJesus Soto, Patricia Melin, Oscar Castillo. 000824759 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2018] 000824759 300__ $$a1 online resource :$$billustrations. 000824759 336__ $$atext$$btxt$$2rdacontent 000824759 337__ $$acomputer$$bc$$2rdamedia 000824759 338__ $$aonline resource$$bcr$$2rdacarrier 000824759 347__ $$atext file$$bPDF$$2rda 000824759 4901_ $$aSpringBriefs in applied sciences and technology. Computational intelligence 000824759 500__ $$aIncludes index. 000824759 506__ $$aAccess limited to authorized users. 000824759 520__ $$aThis book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. This book describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series prediction. Interval type-2 and type-1 fuzzy systems are used to integrate the outputs of the Ensemble of Interval Type-2 Fuzzy Neural Network models. Genetic Algorithms and Particle Swarm Optimization are the Bio-Inspired algorithms used for the optimization of the fuzzy response integrators. The Mackey-Glass, Mexican Stock Exchange, Dow Jones and NASDAQ time series are used to test of performance of the proposed method. Prediction errors are evaluated by the following metrics: Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Percentage Error and Mean Absolute Percentage Error. The proposed prediction model outperforms state of the art methods in predicting the particular time series considered in this work.  . 000824759 588__ $$aVendor-supplied metadata. 000824759 650_0 $$aTime-series analysis. 000824759 650_0 $$aFuzzy algorithms. 000824759 7001_ $$aMelin, Patricia,$$d1962-$$eauthor. 000824759 7001_ $$aCastillo, Oscar,$$d1959-$$eauthor. 000824759 77608 $$iPrint version: $$z3319712632$$z9783319712635$$w(OCoLC)1007057686 000824759 830_0 $$aSpringBriefs in applied sciences and technology.$$pComputational intelligence. 000824759 852__ $$bebk 000824759 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-71264-2$$zOnline Access$$91397441.1 000824759 909CO $$ooai:library.usi.edu:824759$$pGLOBAL_SET 000824759 980__ $$aEBOOK 000824759 980__ $$aBIB 000824759 982__ $$aEbook 000824759 983__ $$aOnline 000824759 994__ $$a92$$bISE