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A complex-valued RTRL algorithm for recurrent neural networks.

Su Lee Goh·Danilo P Mandic
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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.

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AlgorithmsArtificial IntelligenceNeural Networks, ComputerNeuronsNonlinear DynamicsSignal Processing, Computer-AssistedJournal Article

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Su Lee Goh, Danilo P Mandic. 2004. A complex-valued RTRL algorithm for recurrent neural networks.. https://doi.org/10.1162/0899766042321779

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