Supervised learning with complex-valued neural networks /

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Bibliographic Details
Author / Creator:Suresh, Sundaram.
Imprint:Berlin ; New York : Springer, ©2013.
Description:1 online resource.
Language:English
Series:Studies in computational intelligence, 1860-949X ; 421
Studies in computational intelligence ; 421.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11077137
Hidden Bibliographic Details
Other authors / contributors:Sundararajan, Narasimhan.
Savitha, Ramasamy.
ISBN:9783642294914
364229491X
3642294901
9783642294907
9783642294907
Notes:Includes bibliographical references.
Summary: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.

MARC

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300 |a 1 online resource. 
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490 1 |a Studies in computational intelligence,  |x 1860-949X ;  |v 421 
505 0 0 |t Introduction --  |t Fully Complex-valued Multi Layer Perceptron Networks --  |t A Fully Complex-valued Radial Basis Function Network and Its Learning Algorithm --  |t Fully Complex-valued Relaxation Networks --  |t Performance Study on Complex-valued Function Approximation Problems --  |t Circular Complex-valued Extreme Learning Machine Classifier --  |t Performance Study on Real-valued Classification Problems --  |t Complex-valued Self-regulatory Resource Allocation Network (CSRAN). 
504 |a Includes bibliographical references. 
520 |a 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. 
650 0 |a Supervised learning (Machine learning)  |0 http://id.loc.gov/authorities/subjects/sh94008290 
650 0 |a Neural networks (Computer science)  |0 http://id.loc.gov/authorities/subjects/sh90001937 
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