Overview
- This book covers recent developments and applications in the area of complex-valued neural networks
- This book especially addresses researchers and engineers working in the areas of neural networks, communications and signal processing, and also researchers working in the areas of image processing especially in medical image processing
- Written by leading experts in the field
Part of the book series: Studies in Computational Intelligence (SCI, volume 421)
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About this book
Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks. Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computation time of the training process is critical, a fast learning complex-valued neural network called as a fully complex-valued relaxation network along with its learning algorithm has been presented. The presence of orthogonal decision boundaries helps complex-valued neural networks to outperform real-valued networks in performing classification tasks. This aspect has been highlighted. The performances of various complex-valued neural networks are evaluated on a set of benchmark and real-world function approximation and real-valued classification problems.
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Keywords
Table of contents (9 chapters)
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Front Matter
Authors and Affiliations
Bibliographic Information
Book Title: Supervised Learning with Complex-valued Neural Networks
Authors: Sundaram Suresh, Narasimhan Sundararajan, Ramasamy Savitha
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-29491-4
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2013
Hardcover ISBN: 978-3-642-29490-7Published: 28 July 2012
Softcover ISBN: 978-3-642-42679-7Published: 09 August 2014
eBook ISBN: 978-3-642-29491-4Published: 28 July 2012
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XXII, 170
Topics: Computational Intelligence, Signal, Image and Speech Processing