A state-space definition of fading memory is introduced that extends incremental input-to-output stability via a memory kernel, is implied by incremental input-to-state stability under bounded inputs, and holds for current-driven memristor models.
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AR-KAN combines a pre-trained AR module with KAN to reduce redundancy while preserving temporal features, delivering lower probabilistic approximation error and stronger forecasting results on synthetic almost-periodic signals and real datasets.
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State-space fading memory
A state-space definition of fading memory is introduced that extends incremental input-to-output stability via a memory kernel, is implied by incremental input-to-state stability under bounded inputs, and holds for current-driven memristor models.
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AR-KAN: Autoregressive-Weight-Enhanced Kolmogorov-Arnold Network for Time Series Forecasting
AR-KAN combines a pre-trained AR module with KAN to reduce redundancy while preserving temporal features, delivering lower probabilistic approximation error and stronger forecasting results on synthetic almost-periodic signals and real datasets.