03781nam a22004815i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050000900172072001600181072002300197072001500220100015200235245017200387250001800559264006900577300006500646336002600711337002600737338003600763347002400799490006100823505039100884520141201275650003902687650003602726650003902762650003602801700008502837700008502922710003403007773002603041776003603067776003603103830006803139856004603207912001403253912001503267999001703282978-3-031-71101-5DE-He21320251030113112.0cr nn 008mamaa240928s2024    sz |    s    |||| 0|eng d  a97830317110159978-3-031-71101-57 a10.1007/978-3-031-71101-52doi 4aQ342 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema1 aMelin, Patricia.eauthor.0(orcid)0000-0001-5798-14261https://orcid.org/0000-0001-5798-14264aut4http://id.loc.gov/vocabulary/relators/aut97491210aClustering, Classification, and Time Series Prediction by Using Artificial Neural Networksh[electronic resource] /cby Patricia Melin, Martha Ramirez, Oscar Castillo.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aVIII, 74 p. 21 illus., 20 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringerBriefs in Computational Intelligence,x2625-37120 a1. Introduction to Prediction with Neural Networks -- 2. Literature Review on Prediction with Neural Networks -- 3. Problem Description of Prediction with Neural Networks -- 4. Methodology for Prediction with Neural Networks5 -- Results of Prediction with Neural Networks -- 6. Discussion of Prediction Results with Neural Networks -- 7. Conclusions for Prediction with Neural Networks.  aThis book provides a new model for clustering, classification, and time series prediction by using artificial neural networks to computationally simulate the behavior of the cognitive functions of the brain is presented. This model focuses on the study of intelligent hybrid neural systems and their use in time series analysis and decision support systems. Therefore, through the development of eight case studies, multiple time series related to the following problems are analyzed: traffic accidents, air quality and multiple global indicators (energy consumption, birth rate, mortality rate, population growth, inflation, unemployment, sustainable development, and quality of life). The main contribution consists of a Generalized Type-2 fuzzy integration of multiple indicators (time series) using both supervised and unsupervised neural networks and a set of Type-1, Interval Type-2, and Generalized Type-2 fuzzy systems. The obtained results show the advantages of the proposed model of Generalized Type-2 fuzzy integration of multiple time series attributes. This book is intended to be a reference for scientists and engineers interested in applying type-2 fuzzy logic techniques for solving problems in classification and prediction. We consider that this book can also be used to get novel ideas for new lines of research, or to continue the lines of research proposed by the authors of the book. 0aComputational intelligence.964052 0aEngineering mathematics.96414514aComputational Intelligence.96405224aEngineering Mathematics.9641451 aRamirez, Martha.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9851961 aCastillo, Oscar.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9667672 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303171100808iPrinted edition:z9783031711022 0aSpringerBriefs in Computational Intelligence,x2625-371297407340uhttps://doi.org/10.1007/978-3-031-71101-5  aZDB-2-INR  aZDB-2-SXIT  c63801d63801