Abstract: Two jamming cancellation algorithms are developed based on a stable solution of least squares problem (LSP) provided by regularization. They are based on filtered singular value decomposition (SVD) and modifications of the Greville formula. Both algorithms allow an efficient hardware implementation. Testing results on artificial data modeling difficult real-world situations are also provided

Keywords: jamming cancellation, approximation, least squares problem, stable solution, recurrent solution, neural networks, incremental training, filtered SVD, Greville formula

ACM Classification Keywords: I.5.4 Signal processing, G.1.2 Least squares approximation, I.5.1 Neural nets

Link:

TRAINING A LINEAR NEURAL NETWORK WITH A STABLE LSP SOLUTION FOR JAMMING CANCELLATION

Elena Revunova, Dmitri Rachkovskij

http://www.foibg.com/ijita/vol12/ijita12-3-p04.pdf