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    Home»Machine Learning»[2512.10817] Extrapolation of Periodic Functions Using Binary Encoding of Continuous Numerical Values
    Machine Learning

    [2512.10817] Extrapolation of Periodic Functions Using Binary Encoding of Continuous Numerical Values

    adminBy adminNovember 13, 2026No Comments2 Mins Read
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    [2512.10817] Extrapolation of Periodic Functions Using Binary Encoding of Continuous Numerical Values
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    [Submitted on 11 Dec 2025 (v1), last revised 11 Nov 2026 (this version, v2)]

    View a PDF of the paper titled Extrapolation of Periodic Functions Using Binary Encoding of Continuous Numerical Values, by Brian P. Powell and 6 other authors

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    Abstract:We introduce Normalized Base-2 Encoding (NB2E) as a method for encoding continuous numerical values and demonstrate that, using this input encoding, vanilla multi-layer perceptrons (MLP) successfully extrapolate four periodic waveforms without prior knowledge of their functional form. We contrast this finding with fixed Fourier encoding, which extrapolates well when the number of signal cycles over the normalized domain is an even integer and loses coherence at most other values, whereas NB2E degrades gradually away from those values and, except near odd cycle counts, remains substantially more accurate. These findings are limited to a single MLP architecture trained on one synthetic, exactly periodic, one-dimensional signal at a time, with extrapolation extending only to the end of the normalized domain. We do not propose a mechanism for this phenomenon. Rather, we present empirical observations which merit further study.

    Submission history

    From: Brian Powell [view email]
    [v1]
    Thu, 11 Dec 2025 17:08:28 UTC (7,547 KB)
    [v2]
    Wed, 11 Nov 2026 18:42:44 UTC (2,608 KB)

    Binary continuous Encoding Extrapolation Functions Numerical Periodic Values
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