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2 Fundamenta Informaticae

2 1998

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The modified Oja-RLS algorithm, stochastic convergence analysis and application for image compression

Skarbek W., Pietrowcew A., Sikora R.

Fundamenta Informaticae

1998

Vol. 36, nr 4

345-365

The results of theoretical analysis for stochastic convergence of the modified Oja-RLS learning rule are presented. The rule is used to find Karhunen Loeve Transform. Based on this algorithm an image compression scheme is developed by combining approximated 2D KLT transform and JPEG standard quantization and entropy coding stages. Though 2D KLT transform is of higher complexity than 2D DCT, the resulting PSNR quality of reconstructed images is better even by 2[dB].

Stability analysis of Oja-RLS learning rule

Sikora R., Skarbek W.

Fundamenta Informaticae

1998

Vol. 34, nr 4

441-453

It is shown that the discrete time dynamical system defined by the Oja-RLS algorithm is stable in the closed ring K(0,9/8) - K(0,8/9) if only the initial gain B0 is bounded by (2B)-1, where B=b2 and b is the bound for the learning sequence. It is rigorously proved that automatically computed gains Bn in Oja-RLS scheme converge to zero with the rate 1/n, almost surely.