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Titlebook: Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks; Patricia Melin,Martha Ramirez,Oscar Castillo B

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發(fā)表于 2025-3-21 19:56:34 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks
編輯Patricia Melin,Martha Ramirez,Oscar Castillo
視頻videohttp://file.papertrans.cn/243/242177/242177.mp4
概述Presents a new model for the clustering, classification, and time series prediction by using artificial neural networks .Focuses on the study of intelligent hybrid neural systems and their use in time
叢書名稱SpringerBriefs in Applied Sciences and Technology
圖書封面Titlebook: Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks;  Patricia Melin,Martha Ramirez,Oscar Castillo B
描述.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 solvin
出版日期Book 2024
關(guān)鍵詞Computational Intelligence; Clustering; Classification; Time Series Prediction ; Artificial Neural Netwo
版次1
doihttps://doi.org/10.1007/978-3-031-71101-5
isbn_softcover978-3-031-71100-8
isbn_ebook978-3-031-71101-5Series ISSN 2191-530X Series E-ISSN 2191-5318
issn_series 2191-530X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 21:21:44 | 只看該作者
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發(fā)表于 2025-3-22 04:07:51 | 只看該作者
Book 2024alized 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 solvin
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發(fā)表于 2025-3-22 07:22:44 | 只看該作者
Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks
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發(fā)表于 2025-3-22 10:57:24 | 只看該作者
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發(fā)表于 2025-3-22 13:17:16 | 只看該作者
Smart Innovation, Systems and TechnologiesThe human being can solve multiple problems simultaneously using different parts of the brain, which by itself represents a complex subject of study. For several decades, a great challenge has attracted the attention of the scientific community, causing different areas of study to join forces to understand how the human brain works.
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發(fā)表于 2025-3-23 01:36:36 | 只看該作者
Smart Innovation, Systems and TechnologiesIn general, our proposal consists of combining several methods to create a computational model that simulates the cognitive functioning of the human brain; focusing on the brain processes implicit in the mental routine that the human being performs to decision-making, where each mental process is directed towards the solution of a specific task.
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發(fā)表于 2025-3-23 05:36:55 | 只看該作者
Data Discretization for?Data Stream MiningFor the case studies where the neural networks experiments were use, we performed 30 executions for each experiment.
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