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Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks (SpringerBriefs in Applied Sciences and Technology) - Softcover
- PublisherSpringer
- Publication date2024
- ISBN 10 3031711009
- ISBN 13 9783031711008
- BindingPaperback
- LanguageEnglish
- Number of pages82
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Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks (SpringerBriefs in Applied Sciences and Technology)
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Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks (SpringerBriefs in Applied Sciences and Technology)
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Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks (Paperback)
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Springer International Publishing AG, Cham, 2024
ISBN 10: 3031711009
ISBN 13: 9783031711008
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Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783031711008
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Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks (SpringerBriefs in Applied Sciences and Technology)
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Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks
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Springer, Berlin, Springer Nature Switzerland, Springer, 2024
ISBN 10: 3031711009
ISBN 13: 9783031711008
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 74 pp. Englisch. Seller Inventory # 9783031711008
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Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks
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Springer Nature Switzerland, Springer Nature Switzerland, 2024
ISBN 10: 3031711009
ISBN 13: 9783031711008
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783031711008
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Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks
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Springer-Nature New York Inc, 2024
ISBN 10: 3031711009
ISBN 13: 9783031711008
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Paperback. Condition: Brand New. 72 pages. 9.26x6.11x9.21 inches. In Stock. Seller Inventory # x-3031711009
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Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks (Paperback)
Published by
Springer International Publishing AG, Cham, 2024
ISBN 10: 3031711009
ISBN 13: 9783031711008
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Paperback
Seller: AussieBookSeller, Truganina, VIC, Australia
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Paperback. Condition: new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9783031711008
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