04018nam a22005295i 4500 978-3-031-71101-5 DE-He213 20251030113112.0 cr nn 008mamaa 240928s2024 sz | s |||| 0|eng d 9783031711015 978-3-031-71101-5 10.1007/978-3-031-71101-5 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Melin, Patricia. author. (orcid)0000-0001-5798-1426 https://orcid.org/0000-0001-5798-1426 aut http://id.loc.gov/vocabulary/relators/aut 74912 Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks [electronic resource] / by Patricia Melin, Martha Ramirez, Oscar Castillo. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. VIII, 74 p. 21 illus., 20 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda SpringerBriefs in Computational Intelligence, 2625-3712 1. 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. This 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. Computational intelligence. 64052 Engineering mathematics. 64145 Computational Intelligence. 64052 Engineering Mathematics. 64145 Ramirez, Martha. author. aut http://id.loc.gov/vocabulary/relators/aut 85196 Castillo, Oscar. author. aut http://id.loc.gov/vocabulary/relators/aut 66767 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031711008 Printed edition: 9783031711022 SpringerBriefs in Computational Intelligence, 2625-3712 74073 https://doi.org/10.1007/978-3-031-71101-5 ZDB-2-INR ZDB-2-SXIT 63801 63801