Supervised Learning with Complex-valued Neural Networks
(Sprache: Englisch)
A new generation of neural networks is needed in telecommunications, medical imaging and signal processing as signals become more complex and nonlinear. This survey of the latest complex-valued networks includes learning algorithms and new architectures.
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A new generation of neural networks is needed in telecommunications, medical imaging and signal processing as signals become more complex and nonlinear. This survey of the latest complex-valued networks includes learning algorithms and new architectures.
Klappentext zu „Supervised Learning with Complex-valued Neural Networks “
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.
Inhaltsverzeichnis zu „Supervised Learning with Complex-valued Neural Networks “
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).
Bibliographische Angaben
- Autoren: Sundaram Suresh , Narasimhan Sundararajan , Ramasamy Savitha
- 2014, 2013, XXII, 170 Seiten, Masse: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3642426794
- ISBN-13: 9783642426797
Sprache:
Englisch
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