Title :
An echo state network architecture based on volterra filtering and PCA with application to the channel equalization problem
Author :
Boccato, Levy ; Lopes, Amauri ; Attux, Romis ; Zuben, Fernando José Von
Author_Institution :
Dept. of Comput. Eng. & Ind. Autom. (DCA), Univ. of Campinas, Sao Paulo, Brazil
fDate :
July 31 2011-Aug. 5 2011
Abstract :
Echo state networks represent a promising alternative to the classical approaches involving recurrent neural networks, as they ally processing capability, due to the existence of feedback loops within the dynamical reservoir, with a simplified training process. However, the existing networks cannot fully explore the potential of the underlying structure, since the outputs are computed via linear combinations of the internal states. In this work, we propose a novel architecture for an echo state network that employs the Volterra filter structure in the output layer together with the Principal Component Analysis technique. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. The proposed architecture has been analyzed in the context of the channel equalization problem, and the obtained results highlight the adequacy and the advantages of the novel network, which achieved a convincing performance, overcoming the other echo state networks, especially in the most challenging scenarios.
Keywords :
equalisers; nonlinear filters; principal component analysis; recurrent neural nets; signal processing; telecommunication computing; PCA; Volterra filtering; channel equalization problem; echo state network architecture; principal component analysis technique; recurrent neural networks; Context; Equalizers; Principal component analysis; Proposals; Recurrent neural networks; Reservoirs; Training;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033273