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OverviewThe recent interest in artificial neural networks has motivated the publication of numerous books, including selections of research papers and textbooks presenting the most popular neural architectures and learning schemes. Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications presents recent developments which can have a very significant impact on neural network research, in addition to the selective review of the existing vast literature on artificial neural networks. This book can be read in different ways, depending on the background, the specialization, and the ultimate goals of the reader. A specialist will find in this book well-defined and easily reproducible algorithms, along with the performance evaluation of various neural network architectures and training schemes. Artificial Neural Networks can also help a beginner interested in the development of neural network systems to build the necessary background in an organized and comprehensive way. The presentation of the material in this book is based on the belief that the successful application of neural networks to real-world problems depends strongly on the knowledge of their learning properties and performance. Neural networks are introduced as trainable devices which have the unique ability to generalize. The pioneering work on neural networks which appeared during the past decades is presented, together with the current developments in the field, through a comprehensive and unified review of the most popular neural network architectures and learning schemes. Efficient LEarning Algorithms for Neural NEtworks (ELEANNE), which can achieve much faster convergence than existing learning algorithms, are among the recent developments explored in this book. A new generalized criterion for the training of neural networks is presented, which leads to a variety of fast learning algorithms. Finally, Artificial Neural Networks presents the development of learning algorithms which determine the minimal architecture of multi-layered neural networks while performing their training. Artificial Neural Networks is a valuable source of information to all researchers and engineers interested in neural networks. The book may also be used as a text for an advanced course on the subject. Full Product DetailsAuthor: Nicolaos Karayiannis , Anastasios N. VenetsanopoulosPublisher: Springer-Verlag New York Inc. Imprint: Springer-Verlag New York Inc. Edition: Softcover reprint of hardcover 1st ed. 1993 Volume: 209 Dimensions: Width: 15.50cm , Height: 2.30cm , Length: 23.50cm Weight: 0.700kg ISBN: 9781441951328ISBN 10: 1441951326 Pages: 440 Publication Date: 03 December 2010 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Out of stock  The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available. Table of Contents1 Introduction.- 2 Neural Network Architectures and Learning Schemes.- 3 ELEANNE: Efficient LEarning Algorithms for Neural NEtworks.- 4 Fast Learning Algorithms for Neural Networks.- 5 ALADIN: Algorithms for Learning and Architecture DetermlNation.- 6 Performance Evaluation of Single-layered Neural Networks.- 7 High-order Neural Networks and Networks with Composite Key Patterns.- 8 Applications of Neural Networks: A Case Study.- 9 Applications of Neural Networks: A Review.- 10 Future Trends and Directions.- References.- Author Index.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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