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Titlebook: Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation; Daniela Sanchez,Patricia Melin Book 2016 The Author(s) 2016 Computat

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發(fā)表于 2025-3-21 18:46:20 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation
編輯Daniela Sanchez,Patricia Melin
視頻videohttp://file.papertrans.cn/427/426141/426141.mp4
概述Introduces a new model of a modular neural network.based on a granular approach.Serves as reference.book for scientists and engineers interested in applying soft computing.Presents recent research.Inc
叢書名稱SpringerBriefs in Applied Sciences and Technology
圖書封面Titlebook: Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation;  Daniela Sanchez,Patricia Melin Book 2016 The Author(s) 2016 Computat
描述.In this book, anew method for hybrid intelligent systems is proposed. The proposed method isbased on a granular computing approach applied in two levels. The techniquesused and combined in the proposed method are modular neural networks (MNNs)with a Granular Computing (GrC) approach, thus resulting in a new concept ofMNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL)and hierarchical genetic algorithms (HGAs) are techniques used in this researchwork to improve results. These techniques are chosen because in other workshave demonstrated to be a good option, and in the case of MNNs and HGAs, thesetechniques allow to improve the results obtained than with their conventionalversions; respectively artificial neural networks and genetic algorithms..
出版日期Book 2016
關(guān)鍵詞Computational Intelligence; Granular Neural Networks; Fuzzy Aggregation; Granular Computing; Hierarchica
版次1
doihttps://doi.org/10.1007/978-3-319-28862-8
isbn_softcover978-3-319-28861-1
isbn_ebook978-3-319-28862-8Series ISSN 2191-530X Series E-ISSN 2191-5318
issn_series 2191-530X
copyrightThe Author(s) 2016
The information of publication is updating

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Book 2016ues used in this researchwork to improve results. These techniques are chosen because in other workshave demonstrated to be a good option, and in the case of MNNs and HGAs, thesetechniques allow to improve the results obtained than with their conventionalversions; respectively artificial neural networks and genetic algorithms..
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2191-530X ther workshave demonstrated to be a good option, and in the case of MNNs and HGAs, thesetechniques allow to improve the results obtained than with their conventionalversions; respectively artificial neural networks and genetic algorithms..978-3-319-28861-1978-3-319-28862-8Series ISSN 2191-530X Series E-ISSN 2191-5318
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Daniela Sanchez,Patricia MelinIntroduces a new model of a modular neural network.based on a granular approach.Serves as reference.book for scientists and engineers interested in applying soft computing.Presents recent research.Inc
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SpringerBriefs in Applied Sciences and Technologyhttp://image.papertrans.cn/h/image/426141.jpg
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