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001 978-3-031-71101-5
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020 _a9783031711015
_9978-3-031-71101-5
024 7 _a10.1007/978-3-031-71101-5
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
100 1 _aMelin, Patricia.
_eauthor.
_0(orcid)0000-0001-5798-1426
_1https://orcid.org/0000-0001-5798-1426
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_974912
245 1 0 _aClustering, Classification, and Time Series Prediction by Using Artificial Neural Networks
_h[electronic resource] /
_cby Patricia Melin, Martha Ramirez, Oscar Castillo.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aVIII, 74 p. 21 illus., 20 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computational Intelligence,
_x2625-3712
505 0 _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.
520 _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.
650 0 _aComputational intelligence.
_964052
650 0 _aEngineering mathematics.
_964145
650 1 4 _aComputational Intelligence.
_964052
650 2 4 _aEngineering Mathematics.
_964145
700 1 _aRamirez, Martha.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985196
700 1 _aCastillo, Oscar.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_966767
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031711008
776 0 8 _iPrinted edition:
_z9783031711022
830 0 _aSpringerBriefs in Computational Intelligence,
_x2625-3712
_974073
856 4 0 _uhttps://doi.org/10.1007/978-3-031-71101-5
912 _aZDB-2-INR
912 _aZDB-2-SXIT
999 _c63801
_d63801