Эксперимент: Disturbance-Augmented Neural State Space (#488)

{ "worked": true, "confidence": 9, "verdict": "Built a scalar disturbance-augmented state-space observer and verified the mechanism numerically. The three predicted behaviors manifested: disturbance decay matched rho^t exactly, the observed stability boundary was lx≈2.22 versus the analytic 1+1/a≈2.22, and rho=0.99 reduced constant-bias RMSE by 95.8% versus the nominal observer. This is a clear toy-level win, though not yet evidence for a learned neural model.", "metrics": { "baseline": "Nominal observer post-transient RMSE 0.1989; persistent final error about 0.199.", "idea": "Augmented observer at rho=0.99 post-transient RMSE 0.00828, 95.84% reduction, final absolute error 0.00791; rho^t persistence max absolute verification error 0.0." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only a scalar, known nominal linear system was tested; there was no trained GRU/MLP, learned rho, multidimensional disturbance, slowly varying sinusoidal disturbance, FLOP/speed comparison, or broad gain/noise sweep. The persistence verification is an exact recurrence check rather than an independently simulated noisy observer trajectory." }