+612 9045 4394
 
CHECKOUT
Artificial Neural Networks : Learning Algorithms, Performance Evaluation, and Applications - Nicolas B. Karayiannis

Artificial Neural Networks

Learning Algorithms, Performance Evaluation, and Applications

Hardcover

Published: 31st December 1992
Ships: 7 to 10 business days
7 to 10 business days
RRP $717.99
$497.25
31%
OFF
or 4 easy payments of $124.31 with Learn more
if ordered within

Other Available Formats (Hide)

  • Paperback View Product Published: 3rd December 2010
    $304.87

The 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 architectural 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 should find in this book well-defined and easily reproducible algorithms which perform the training of neural networks much faster than existing 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 materials 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 decade 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, the book presents the development of learning algorithms which determine the minimal architecture of multi-layered neural networks while performing their training. The text is a valuale 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.

Acknowledgments
Introductionp. 1
Overviewp. 1
Book Organizationp. 4
Neural Network Architectures and Learning Schemesp. 9
Feed-forward Neural Networksp. 11
Feed-back Neural Networksp. 46
Self-organizing Neural Networksp. 67
ELEANNE: Efficient LEarning Algorithms for Neural NEtworksp. 87
Recursive Least-squares Algorithmsp. 90
Efficient Learning Algorithms for Single-layered Neural Networksp. 98
Efficient Learning Algorithms for Multi-layered Neural Networksp. 109
Computational Considerationsp. 121
Experimental Resultsp. 126
Fast Learning Algorithms for Neural Networksp. 141
A Generalized Training Criterionp. 144
Fast Learning Algorithms for Single-layered Neural Networksp. 151
Fast Learning Algorithms for Multi-layered Neural Networksp. 161
Experimental Resultsp. 176
ALADIN: Algorithms for Learning and Architecture DetermINationp. 195
Training Criteriap. 198
Neural Networks with one Hidden Layerp. 199
Neural Networks with Multiple Hidden Layersp. 207
Experimental Resultsp. 209
Performance Evaluation of Single-layered Neural Networksp. 219
Optimal Least-squares Training of Single-layered Neural Networksp. 220
Capacity Considerationsp. 234
Output Nonlinearities and Network Performancep. 247
High-order Neural Networks and Networks with Composite Key Patternsp. 259
High-order Neural Networksp. 260
Neural Networks with Composite Key Patternsp. 279
Capacity Considerationsp. 287
Applications of Neural Networks: A Case Studyp. 299
General Methodology for the Development of Neural Network Systemsp. 300
Application of Neural Networks in Environmental Protectionp. 303
Applications of Neural Networks: A Reviewp. 317
Optimization Problemsp. 319
Image Compressionp. 330
Recognition of Handwritten Signatures, Characters, and Digitsp. 338
Text to Speech Conversionp. 347
Classification Applicationsp. 349
Medical Diagnosisp. 351
Prediction of Secondary Structures of Proteinsp. 354
Weather Forecastingp. 358
Financial Predictionsp. 360
Other Applicationsp. 368
Future Trends and Directionsp. 371
Referencesp. 375
Subject Indexp. 413
Author Indexp. 435
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9780792392972
ISBN-10: 0792392973
Series: KLUWER INTERNATIONAL SERIES IN ENGINEERING AND COMPUTER SCIENCE
Audience: General
Format: Hardcover
Language: English
Number Of Pages: 440
Published: 31st December 1992
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 24.77 x 16.51  x 3.18
Weight (kg): 0.93