A complex-valued nonlinear neural adaptive filter with a gradient adaptive amplitude of the activation function

Autor:	Andrew I, Hanna, Danilo P, Mandic
Rok vydání:	2003
Předmět:	Nonlinear Dynamics Neural Networks Computer Algorithms
Zdroj:	Neural networks : the official journal of the International Neural Network Society. 16(2)
ISSN:	0893-6080
Popis:	A complex-valued nonlinear gradient descent (CNGD) learning algorithm for a simple finite impulse response (FIR) nonlinear neural adaptive filter with an adaptive amplitude of the complex activation function is proposed. This way the amplitude of the complex-valued analytic nonlinear activation function of a neuron in the learning algorithm is made gradient adaptive to give the complex-valued adaptive amplitude nonlinear gradient descent (CAANGD). Such an algorithm is beneficial when dealing with signals that have rich dynamical behavior. Simulations on the prediction of complex-valued coloured and nonlinear input signals show the gradient adaptive amplitude, CAANGD, outperforming the standard CNGD algorithm.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=pmid________::e5b82cbbd752fa1b5ec3303e3d33cc14 https://pubmed.ncbi.nlm.nih.gov/12628604 Zobrazit plný text záznamu Full Text from ScienceDirect