Recurrent Neural Networks_Xiaolin Hu, P. Balasubramaniam.pdf

Recurrent Neural Networks Recurrent Neural Networks Edited by Xiaolin Hu and P. Balasubramaniam I-Tech IV Published by In-Teh In-Teh is Croatian branch of I-Tech Education and Publishing KG, Vienna, Austria. Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2008 In-teh www.in-teh.org Additional copies can be obtained from: [email protected] First published September 2008 Printed in Croatia A catalogue record for this book is available from the University Library Rijeka under no. 111225073 Recurrent Neural Networks, Edited by Xiaolin Hu and P. Balasubramaniam p. cm. ISBN 978-953-7619-08-4 1. Recurrent Neural Networks, Xiaolin Hu and P. Balasubramaniam Preface The research of neural networks has experienced several ups and downs in the 20 th century. The last resurgence is believed to be initiated by several seminal works of Hopfield and Tank in the 1980s, and this upsurge has persisted for three decades. The Hopfield neural networks, either discrete type or continuous type, are actually recurrent neural networks (RNNs). The hallmark of an RNN, in contrast to feedforward neural networks, is the existence of connections from posterior layer(s) to anterior layer(s) or