An Augmented Echo State Network for Nonlinear Adaptive Filtering of Complex Noncircular Signals.

Autor:	Xia, Yili, Jelfs, Beth, Van Hulle, Marc M., Principe, José C., Mandic, Danilo P.
Předmět:	ARTIFICIAL neural networks NONLINEAR statistical models ADAPTIVE control systems INFORMATION filtering systems STATIONARY processes STATISTICAL correlation SIMULATION methods & models
Zdroj:	IEEE Transactions on Neural Networks; 01/01/2011, Vol. 22 Issue 1, p74-83, 10p
Abstrakt:	A novel complex echo state network (ESN), utilizing full second-order statistical information in the complex domain, is introduced. This is achieved through the use of the so-called augmented complex statistics, thus making complex ESNs suitable for processing the generality of complex-valued signals, both second-order circular (proper) and noncircular (improper). Next, in order to deal with nonstationary processes with large nonlinear dynamics, a nonlinear readout layer is introduced and is further equipped with an adaptive amplitude of the nonlinearity. This combination of augmented complex statistics and enhanced adaptivity within ESNs also facilitates the processing of bivariate signals with strong component correlations. Simulations in the prediction setting on both circular and noncircular synthetic benchmark processes and real-world noncircular and nonstationary wind signals support the analysis. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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