LEADER 03656nam 22005655 450 001 9910890185703321 005 20250626164025.0 010 $a9783031711015 010 $a3031711017 024 7 $a10.1007/978-3-031-71101-5 035 $a(MiAaPQ)EBC31692543 035 $a(Au-PeEL)EBL31692543 035 $a(CKB)36231101300041 035 $a(DE-He213)978-3-031-71101-5 035 $a(EXLCZ)9936231101300041 100 $a20240928d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aClustering, Classification, and Time Series Prediction by Using Artificial Neural Networks /$fby Patricia Melin, Martha Ramirez, Oscar Castillo 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (82 pages) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3712 311 08$a9783031711008 311 08$a3031711009 327 $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. 330 $aThis book provides a new model for clustering, classification, and time series prediction by using artificial neural networks to computationally simulate the behavior of the cognitive functions of the brain is presented. This model focuses on the study of intelligent hybrid neural systems and their use in time series analysis and decision support systems. Therefore, through the development of eight case studies, multiple time series related to the following problems are analyzed: traffic accidents, air quality and multiple global indicators (energy consumption, birth rate, mortality rate, population growth, inflation, unemployment, sustainable development, and quality of life). The main contribution consists of a Generalized Type-2 fuzzy integration of multiple indicators (time series) using both supervised and unsupervised neural networks and a set of Type-1, Interval Type-2, and Generalized Type-2 fuzzy systems. The obtained results show the advantages of the proposed model of Generalized Type-2 fuzzy integration of multiple time series attributes. This book is intended to be a reference for scientists and engineers interested in applying type-2 fuzzy logic techniques for solving problems in classification and prediction. We consider that this book can also be used to get novel ideas for new lines of research, or to continue the lines of research proposed by the authors of the book. 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3712 606 $aComputational intelligence 606 $aEngineering mathematics 606 $aComputational Intelligence 606 $aEngineering Mathematics 615 0$aComputational intelligence. 615 0$aEngineering mathematics. 615 14$aComputational Intelligence. 615 24$aEngineering Mathematics. 676 $a006.3 700 $aMelin$b Patricia$0762263 701 $aRamirez$b Martha$01771714 701 $aCastillo$b Oscar$0762265 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910890185703321 996 $aClustering, Classification, and Time Series Prediction by Using Artificial Neural Networks$94264199 997 $aUNINA