A Complex-Valued RTRL Algorithm for Recurrent Neural Networks
Abstract
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.
Cite
Text
Goh and Mandic. "A Complex-Valued RTRL Algorithm for Recurrent Neural Networks." Neural Computation, 2004. doi:10.1162/0899766042321779Markdown
[Goh and Mandic. "A Complex-Valued RTRL Algorithm for Recurrent Neural Networks." Neural Computation, 2004.](https://mlanthology.org/neco/2004/goh2004neco-complexvalued/) doi:10.1162/0899766042321779BibTeX
@article{goh2004neco-complexvalued,
title = {{A Complex-Valued RTRL Algorithm for Recurrent Neural Networks}},
author = {Goh, Su Lee and Mandic, Danilo P.},
journal = {Neural Computation},
year = {2004},
pages = {2699-2713},
doi = {10.1162/0899766042321779},
volume = {16},
url = {https://mlanthology.org/neco/2004/goh2004neco-complexvalued/}
}