Recurrent Neural Networks for Temporal Data Processing
by Hubert Cardot
|Published Date: 2011, February|
|Total Pages: 108|
About the book Recurrent Neural Networks for Temporal Data Processing
Recurrent Neural Networks for Temporal Data Processing is a COMPUTER ENGINEERING book which was published by IN-TECH on 2011, February . Hubert Cardot is the author of this book. This book is written in English and has 108 number of pages.
By presenting the latest research work the book demonstrates how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters. The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the ability to naturally take time into account. Valuable approximation results have been obtained for dynamical systems.