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Abstract
We consider already-trained discrete autoregressive neural networks in their most general representations, with the exclusion of time-varying input though, and we provide tight sufficient conditions and elementary proofs for the existence of an attractor, uniqueness, and global convergence. Those conditions can be used as easy-to-check criteria when convergence (or not) of long-range predictions is desirable.
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Affiliation(s)
- Patrick Leoni
- University of Southern Denmark, Department of Business and Economics, 55 DK-5230, Odense M, Denmark
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