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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

April 2007, Vol. 19, No. 4, Pages 1039-1055
(doi: 10.1162/neco.2007.19.4.1039)
© 2007 Massachusetts Institute of Technology
An Augmented Extended Kalman Filter Algorithm for Complex-Valued Recurrent Neural Networks
Article PDF (191.44 KB)
Abstract

An augmented complex-valued extended Kalman filter (ACEKF) algorithm for the class of nonlinear adaptive filters realized as fully connected recurrent neural networks is introduced. This is achieved based on some recent developments in the so-called augmented complex statistics and the use of general fully complex nonlinear activation functions within the neurons. This makes the ACEKF suitable for processing general complex-valued nonlinear and nonstationary signals and also bivariate signals with strong component correlations. Simulations on benchmark and real-world complex-valued signals support the approach.