Supervised Learning with Complex-valued Neural Networks - Narasimhan Sundararajan,Sundaram Suresh,Ramasamy Savitha
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Toimitus 12-18 arkipäivässä
30 päivän palautusoikeus
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 impor ... Täydellinen kuvaus
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Kuvaus
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.
Lisätietoja
| Kirjoittaja | Narasimhan Sundararajan, Sundaram Suresh, Ramasamy Savitha |
|---|---|
| Julkaisija | Springer Berlin Heidelberg |
| Series | Studies in Computational Intelligence |
| Julkaisuvuosi | 2014 |
| Kannen tyyppi | Pehmeäkantinen |
| EAN | 9783642426797 |