Title :
A hierarchical feedforward adaptive filter for system identification
Author :
Boukis, Christos G. ; Mandic, Danilo P. ; Constantinides, Anthony G.
Author_Institution :
Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK
Abstract :
An architecture for adaptive filtering based upon the previously introduced hierarchical least mean square algorithm is proposed. This pyramidal architecture incorporates sparse connections between the architectural layers with a certain variable degree of overlapping between the neighboring subfilters of the same level. A learning algorithm for this class of structures is derived, based on the back-propagation algorithm for temporal feedforward networks with linear neurons. Further, a class of normalized algorithms for this class is derived. The analysis and simulations show the proposed algorithms outperform the existing ones.
Keywords :
adaptive signal processing; backpropagation; feedforward; filtering theory; identification; least mean squares methods; Taylor series expansion; adaptive filtering; architectural layers; backpropagation algorithm; global gradient descent; hierarchical LMS algorithm; hierarchical feedforward adaptive filter; hierarchical least mean square algorithm; learning algorithm; learning rate; linear neurons; normalized algorithms; output error; pyramidal architecture; real-time adaptive filtering; signal processing; simulation results; sparse connections; subfilters; system identification; temporal feedforward networks; weight updating techniques; Adaptive filters; Adaptive signal processing; Biomedical signal processing; Feedforward neural networks; Finite impulse response filter; Least squares approximation; Neural networks; Neurons; Signal processing algorithms; System identification;
Conference_Titel :
Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on
Print_ISBN :
0-7803-7616-1
DOI :
10.1109/NNSP.2002.1030038
