Supervised Learning with Complex-Valued Neural Networks
| Author/creator | Suresh, Sundaramurthy Author |
| Other author | Sundararajan,Narasimhan Author |
| Other author | Savitha,Ramasamy Author |
| Format | Electronic |
| Publication Info | New York : Springer |
| Description | xxii, 170 p. ill 23.500 x 015.500 cm. |
| Supplemental Content | Full text available from SpringerLINK Studies in Computational Intelligence Contemporary (1997-present) |
| Subjects |
| Series | Studies in Computational Intelligence Ser. 421 |
| Summary | Annotation 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. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| ISBN | 9783642294907 |
| ISBN | 3642294901 (Trade Cloth) Active Record |
| Standard identifier# | 9783642294907 |
| Stock number | 9783642294907 00024965 |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |