Автор: Igor Aizenberg; Naum N. Aizenberg; Joos P.L. Vande Название: Multi-Valued and Universal Binary Neurons ISBN: 0792378245 ISBN-13(EAN): 9780792378242 Издательство: Springer Рейтинг: Цена: 181630.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Deals with 2 types of neurons: multi-valued neurons and universal binary neurons. Networks with such neurons exhibit a broad functionality. This book proves the broad applicability of these networks using several case studies in fields of application: image processing, edge detection, image enhancement, super resolution, and, pattern recognition.
Автор: Frank H. Eeckman Название: Computation in Neurons and Neural Systems ISBN: 0792394658 ISBN-13(EAN): 9780792394655 Издательство: Springer Рейтинг: Цена: 243800.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Contains papers which represent a cross-section of the state-of-the-art research work in the field of computational neuroscience, including coverage of analysis and modelling work, as well as results of new biological experimentation.
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 Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
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