Resource-Constrained Adaptive Sampling for Variance Monitoring: Comparing Rule-Based, MLP, and LSTM Acquisition Policies

Automated monitoring must balance detection delay and measurement use. This study compares rule-based, feedforward multilayer perceptron (MLP), and long short-term memory (LSTM) acquisition policies coupled to a common exponentially weighted moving average (EWMA) variance detector. Experiments 1 and 2 comprise 714,000 and 660,000 test trajectories, respectively. Closely matched rule and LSTM configurations achieve in-control average run lengths of 369.97 and 370.11, consumption of 4.918 and 4.917 observations per epoch, and weighted detection delays of 18.860 and 18.864 epochs; the conditional 95% bootstrap confidence interval for the difference in their weighted delays includes zero. A third experiment repeats optimization, selection, calibration, and testing ten times per family, comparing a search budget of 512 candidate evaluations with one of 30 CPU seconds. The mean LSTM-minus-rule delay contrasts are −0.219 and +0.206 epochs, respectively, with across-run 95% intervals [−0.723, 0.285] and [−0.295, 0.708]. Scenario weights, measurement prices, and acquisition duration further affect policy comparisons. The findings characterize the evaluated finite search procedures and operating points within a common detector; they establish neither intrinsic superiority nor equivalence of the policy architectures.