SOURCE-LINKED INTELLIGENCE
Learning Fractional-Order Dynamics from a Single Trajectory
Many real-world processes exhibit long-range dependence, where the current state depends on a slowly decaying trace of past states rather than on the most recent state alone. This paper studies system identification for discrete-time fractional-order linear time-invariant systems from a single observed trajectory of length $t$, a setting that captures such non-Markovian dynamics through the Grünwald--Letnikov difference operator. Unlike Markovian systems, fractional-order systems couple estimation across the entire history, making both statistical analysis and practical identification more cha
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T05:02:37.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.