Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

November 1, 2002, Vol. 14, No. 11, Pages 2693-2707
(doi: 10.1162/089976602760408026)
© 2002 Massachusetts Institute of Technology
Data-Reusing Recurrent Neural Adaptive Filters
Article PDF (1.19 MB)
Abstract

A class of data-reusing learning algorithms for real-time recurrent neural networks (RNNs) is analyzed. The analysis is undertaken for a general sigmoid nonlinear activation function of a neuron for the real time recurrent learning training algorithm. Error bounds and convergence conditions for such data-reusing algorithms are provided for both contractive and expansive activation functions. The analysis is undertaken for various configurations that are generalizations of a linear structure infinite impulse response adaptive filter.