In this paper, the dynamical behavior of Oja's neural netowrk is analyzed. Oja's net has been traditionally studied in the continuous-time context via some simplification procedures, some of them concerning the asymptotic behavior of the learning gain. The contribution of the paper is the study of a deterministic discrete-time (DDT) version, preserving the discrete-time form of the original network and allowing a more realistic behavior of the learning gain. As a consequence, the discrete-time nature of the new model leads to results which are drastically different to the ones known for the continuous-time formulation. Simulation examples support the presented results.
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