Supervised Learning with Complex-valued Neural Networks
Springer Berlin (Verlag)
978-3-642-29490-7 (ISBN)
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.
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).
Erscheint lt. Verlag | 28.7.2012 |
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Reihe/Serie | Studies in Computational Intelligence |
Zusatzinfo | XXII, 170 p. |
Verlagsort | Berlin |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 431 g |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
Technik ► Elektrotechnik / Energietechnik | |
Schlagworte | Adaptive Beam-Forming • Batch/Sequential Learning • Complex-Valued Multi-Layer Perception • Complex-Valued Radial Basis Function Network • Fast Learning Algorithm • Meta-Cognition • Neuronale Netze • Quadrature Amplitude Modulation • Real-Valued Classification |
ISBN-10 | 3-642-29490-1 / 3642294901 |
ISBN-13 | 978-3-642-29490-7 / 9783642294907 |
Zustand | Neuware |
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