Supervised Learning with Complex-valued Neural Networks by Narasimhan Sundararaj

Description: Supervised Learning with Complex-valued Neural Networks by Narasimhan Sundararajan, Ramasamy Savitha, Sundaram Suresh 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. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. Notes This book covers recent developments and applications in the area of complex-valued neural networksThis 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 Back Cover 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. Table of Contents Introduction.- Fully Complex-valued Multi Layer Perceptron Networks.- Fully Complex-valued Radial Basis Function Networks.- Performance Study on Complex-valued Function Approximation Problems.- Circular Complex-valued Extreme Learning Machine Classifier.- Performance Study on Real-valued Classification Problems.- Complex-valued Self-regulatory Resource Allocation Network.- Conclusions and Scope for FutureWorks (CSRAN). Feature 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 Details ISBN3642294901 Author Sundaram Suresh Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Series Studies in Computational Intelligence Year 2012 ISBN-10 3642294901 ISBN-13 9783642294907 Format Hardcover Imprint Springer-Verlag Berlin and Heidelberg GmbH & Co. K Place of Publication Berlin Country of Publication Germany DEWEY 006.32 Publication Date 2012-07-28 Short Title SUPERVISED LEARNING W/COMPLEX- Language English Media Book Series Number 421 Pages 170 Edition 2013th Illustrations XXII, 170 p. DOI 10.1007/978-3-642-29491-4 Edition Description 2013 ed. 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