Supervised learning with complex-valued neural networks
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Bibliographic Information
Supervised learning with complex-valued neural networks
（Studies in computational intelligence, 421）
Springer, c2013
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Includes bibliographical references
Description and Table of Contents
Description
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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