Abstract
In this paper, the dynamical behavior of Oja’s neural network [7] 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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© 1999 Springer-Verlag Wien
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Zufiria, P.J. (1999). Influence of the Learning Gain on the Dynamics of Oja’s Neural Network. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6384-9_18
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DOI: https://doi.org/10.1007/978-3-7091-6384-9_18
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83364-3
Online ISBN: 978-3-7091-6384-9
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