Theory and design of adaptive filters
Theory and design of adaptive filters
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Adaptive Filtering: Algorithms and Practical Implementation
Adaptive Filtering: Algorithms and Practical Implementation
An augmented CRTRL for complex-valued recurrent neural networks
Neural Networks
Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models
Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models
ARMA Prediction of Widely Linear Systems by Using the Innovations Algorithm
IEEE Transactions on Signal Processing - Part II
Second-order statistics of complex signals
IEEE Transactions on Signal Processing
MUSIC-like estimation of direction of arrival for noncircular sources
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Widely linear estimation with complex data
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Complex ICA by Negentropy Maximization
IEEE Transactions on Neural Networks
Complex blind source extraction from noisy mixtures using second-order statistics
IEEE Transactions on Circuits and Systems Part I: Regular Papers
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A recursive learning algorithm for the training of widely linear infinite impulse response complex valued adaptive filters is proposed. The use of so called augmented complex statistics makes this algorithm suitable for the processing of both second order circular (proper) and noncircular (improper) signals. A closed form solution for the bound on the stepsize is provided, and the small stepsize assumption in the derivation is used to reduce the computational complexity. Simulations for both synthetic and real-world circular and noncircular signals are provided in the prediction setting, illustrating the benefits of the proposed algorithm when modelling general complex signals.