Neural Networks in the Analysis and Design of Structures, Zenon Waszczysznk

Автор: Brian D. Ripley
Название: Pattern Recognition and Neural Networks
ISBN: 0521717701 ISBN-13(EAN): 9780521717700
Издательство: Cambridge Academ
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Цена: 7762.00 р.
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Описание: This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. Valuable advice is included on both theory and applications, while case studies based on real data sets help readers develop their understanding. All data sets are available from www.stats.ox.ac.uk/~ripley/PRbook/

Автор: Alan Dennis, Barbara Haley Wixom, Roberta M. Roth
Название: Systems Analysis and Design, 6th Edition
ISBN: 1118897846 ISBN-13(EAN): 9781118897843
Издательство: Wiley
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Цена: 28670.00 р.
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Описание: The 6th Edition of Systems Analysis and Design continues to offer a hands-on approach to SAD while focusing on the core set of skills that all analysts must possess.

Автор: Jin-Liang Wang; Huai-Ning Wu; Tingwen Huang; Shun-
Название: Analysis and Control of Coupled Neural Networks with Reaction-Diffusion Terms
ISBN: 9811049068 ISBN-13(EAN): 9789811049064
Издательство: Springer
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Цена: 19564.00 р.
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Описание: This book introduces selected recent findings on the analysis and control of dynamical behaviors for coupled reaction-diffusion neural networks. It presents novel research ideas and essential definitions concerning coupled reaction-diffusion neural networks, such as passivity, adaptive coupling, spatial diffusion coupling, and the relationship between synchronization and output strict passivity. Further, it gathers research results previously published in many flagship journals, presenting them in a unified form. As such, the book will be of interest to all university researchers and graduate students in Engineering and Mathematics who wish to study the dynamical behaviors of coupled reaction-diffusion neural networks.

Автор: Bishop, Christopher M.
Название: Neural Networks for Pattern Recognition
ISBN: 0198538642 ISBN-13(EAN): 9780198538646
Издательство: Oxford Academ
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Цена: 13939.00 р.
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Описание: This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest applicability for neural networks. By focusing on pattern recognition, the book provides a much more extensive treatment of many topics than is available in earlier books.

Автор: Leonid Ponomarenko; Che Soong Kim; Agassi Melikov
Название: Performance Analysis and Optimization of Multi-Traffic on Communication Networks
ISBN: 364242340X ISBN-13(EAN): 9783642423406
Издательство: Springer
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Цена: 18167.00 р.
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Описание: Communication has became one of the most dynamical branches of an infrastructure of a modern society. This book contains a unique approach to define the effectiveness of various access strategies depending on real load of different type of calls.

Автор: Alexander Dudin; Valentina Klimenok; Gennadiy Tsar
Название: Modern Probabilistic Methods for Analysis of Telecommunication Networks
ISBN: 3642359795 ISBN-13(EAN): 9783642359798
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book constitutes the refereed proceedings of the International Conference on Modern Probabilistic Methods for Analysis of Telecommunication Networks, Belarusian Winter Workshop in Queueing Theory, BWWQT 2013, held in Minsk, Belarus, in January 2013.

Автор: Jack P. C. Kleijnen
Название: Design and Analysis of Simulation Experiments
ISBN: 144194415X ISBN-13(EAN): 9781441944153
Издательство: Springer
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Цена: 14673.00 р.
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Описание: This book will be the standard reference book on the topic for both researchers and sophisticated practitioners, and it will be used as a textbook in courses or seminars focusing on this topic.

Автор: Huajin Tang; Kay Chen Tan; Zhang Yi
Название: Neural Networks: Computational Models and Applications
ISBN: 3540692258 ISBN-13(EAN): 9783540692256
Издательство: Springer
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Цена: 23757.00 р.
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Описание: Presents theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence.