http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Convergence analysis of recurrent neural networks
1 online resource (xv, 233 pages) : illustrations.
This volume provides a comprehensive study of the convergence of recurrent neural networks, which has been increasingly used in applications relating to associative memory, image processing and pattern recognition. Throughout the book, the authors present their original research results of recent years. While the main objective is to disseminate these results in a unified and comprehensive manner, the book is also written to be helpful to readers requiring basic information, such as methods and tools commonly used in the analysis and design of recurrent neural networks. Audience: This volume is suitable for professionals and researchers, as well as advanced graduate level students in neural computations, and neural networks.
1. Introduction -- 2. Hopfield Recurrent Neural Networks -- 3. Cellular Neural Networks -- 4. Recurrent Neural Networks with Unsaturating Piecewise Linear Activation Functions -- 5. Lotka-Volterra Recurrent Neural Networks with Delays -- 6. Delayed Recurrent Neural Networks with Global Lipschitz Activation Functions -- 7. Other Models of Continuous Time Recurrent Neural Networks -- 8. Discrete Recurrent Neural Networks.
Includes bibliographical references (pages 219-233).