Complex-Valued Neural Networks for Noncoherent Demodulation

Complex-Valued Neural Networks

The usual real-valued artificial neural networks have been applied to various fields such as telecommunications, robotics, bioinformatics, image processing and speech recognition, in which complex numbers (two dimensions) are often used with the Fourier transformation. This indicates the usefulness of complex-valued neural networks whose input and output signals and parameters such as weights a...

Complex-Valued Neural Networks

Novel Complex Valued Neural Networks

In view of many applications, in recent years, there has been increasing interest in complex valued neural networks. In this paper, it is reasoned that transforming real valued signals into complex valued signals (using Discrete Fourier Transform) and processing them in that domain is equivalent to processing real valued signals. This approach could have many advantages. Also neural networks ba...

Complex-valued neural networks for nonlinear complex principal component analysis

Principal component analysis (PCA) has been generalized to complex principal component analysis (CPCA), which has been widely applied to complex-valued data, two-dimensional vector fields, and complexified real data through the Hilbert transform. Nonlinear PCA (NLPCA) can also be performed using auto-associative feed-forward neural network (NN) models, which allows the extraction of nonlinear f...

Supervised Learning with Complex-valued Neural Networks

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