title: 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.
authors.affiliation: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK. su.goh@imperial.ac.uk
authors.forename: Su Lee
authors.initials: SL
authors.lastname: Goh
authors.forename: Danilo P
authors.initials: DP
authors.lastname: Mandic
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted