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Supervised Learning with Complex-valued Neural Networks: (421 Studies in Computational Intelligence)

Supervised Learning with Complex-valued Neural Networks: (421 Studies in Computational Intelligence)

          
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About the 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.

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).


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Product Details
  • ISBN-13: 9783642294907
  • Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
  • Publisher Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Edition: 2013 ed.
  • Language: English
  • Returnable: Y
  • Series Title: 421 Studies in Computational Intelligence
  • Weight: 408 gr
  • ISBN-10: 3642294901
  • Publisher Date: 28 Jul 2012
  • Binding: Hardback
  • Height: 235 mm
  • No of Pages: 170
  • Returnable: Y
  • Spine Width: 15 mm
  • Width: 155 mm


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Supervised Learning with Complex-valued Neural Networks: (421 Studies in Computational Intelligence)
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