Performance-based Adaptation Termination for Preventing Parameter Drift in Adaptive Vibration Suppression

What the math gives to ML

The paper provides a constructive anti-drift mechanism: recursively monitor an exponentially weighted RMS of a performance variable and stop adaptation after performance remains below a threshold for a prescribed dwell interval. Its transferable asset is the separation of an adaptation phase from a maintenance phase using a cheap online performance certificate. In neural networks, this can gate updates to online-adapted parameters in world models, recurrent predictors, controllers, or test-time adaptation modules. The falsifiable prediction is that parameter drift and late-training degradation sharply decrease after the RMS threshold-and-dwell condition is crossed, with negligible monitoring overhead.