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Artificial Neural Networks - Learning Algorithms, Performance Evaluation, and Applications (Hardcover, 1993 ed.)
Loot Price: R4,773
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Artificial Neural Networks - Learning Algorithms, Performance Evaluation, and Applications (Hardcover, 1993 ed.)
Series: The Springer International Series in Engineering and Computer Science, 209
Expected to ship within 10 - 15 working days
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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
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 learningalgorithms,
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.
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