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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Fully Complex-valued Multi Layer Perceptron Networks
verfasst von : Sundaram Suresh, Narasimhan Sundararajan, Ramasamy Savitha
Erschienen in: Supervised Learning with Complex-valued Neural Networks
Verlag: Springer Berlin Heidelberg
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This chapter focuses specifically on the study of different Complex-valued MLP (CMLP) networks and their learning algorithms in detail. Complex-valued MLPs can be broadly classified into two types depending on the way in which the complexvalued signal is handled. They are viz., Split Complex-valued MLP (SC-MLP) and Fully Complex-valued MLP (FC-MLP). In a split complex-valued MLP, the complex-valued inputs and outputs are split into two real-valued inputs using rectangular or polar coordinate systems, though the rectangular co-ordinate based splitting is the most commonly used one. On the other hand, FC-MLP neural networks process the complex-valued input signals, using fully complex-valued activation functions and weights to give fully complex-valued output signals.