| Description |
1 online resource |
| Series |
Studies in computational intelligence, 1860-949X ; 421 |
|
Studies in computational intelligence ; 421.
|
| Contents |
Introduction -- Fully Complex-valued Multi Layer Perceptron Networks -- A Fully Complex-valued Radial Basis Function Network and Its Learning Algorithm -- Fully Complex-valued Relaxation 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 (CSRAN) |
| 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 |
| Analysis |
Engineering |
|
Computational Intelligence |
|
Signal, Image and Speech Processing |
| Bibliography |
Includes bibliographical references |
| Notes |
English |
| Subject |
Supervised learning (Machine learning)
|
|
Neural networks (Computer science)
|
|
Ingénierie.
|
|
Neural networks (Computer science)
|
|
Supervised learning (Machine learning)
|
| Form |
Electronic book
|
| Author |
Sundararajan, Narasimhan
|
|
Savitha, Ramasamy
|
| ISBN |
9783642294914 |
|
364229491X |
|
3642294901 |
|
9783642294907 |
|
