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A Complex-Valued RTRL Algorithm for Recurrent Neural Networks

su.goh{at}imperial.ac.uk, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K.

Danilo P. Mandic

d.mandic{at}imperial.ac.uk, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K.

A complex-valued real-time recurrent learning (CRTRL) algorithm for the class of nonlinear adaptive filters realized as fully connected recurrent neural networks is introduced. The proposed CRTRL is derived for a general complex activation function of a neuron, which makes it suitable for nonlinear adaptive filtering of complex-valued nonlinear and nonstationary signals and complex signals with strong component correlations. In addition, this algorithm is generic and represents a natural extension of the real-valued RTRL. Simulations on benchmark and real-world complex-valued signals support the approach.

This article has been cited by other articles:

				S. L. Goh and D. P. Mandic An augmented extended kalman filter algorithm for complex-valued recurrent neural networks. Neural Comput., April 1, 2007; 19(4): 1039 - 1055. [Abstract] [Full Text] [PDF]