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The model converging error becomes saturated and slightly changes after the around thousand epochs.
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Such a control strategy guarantees a bounded tracking-error with the error converging asymptotically to zero in case that additive disturbances also converge to zero.
All of them converge (validation error rate stop decreasing) in less than 60 epoches, and we do not use any data augmentation in fine-tuning.
The converging speed of error states can be arbitrary turned by assigning the corresponding dynamics of the sliding surfaces.
Finally, the scheme is shown to be second-order accurate the spatial discretization error converging as M−2.
Despite this large mismatch, the state observer performs satisfactorily in converging the estimation error for the case of an example ethylene glycol reactive distillation system.
If the estimation algorithm for a model with a random effect were to successfully converge, standard errors would likely be markedly bigger.
However, when normalized over the entire, large, dataset, the higher training accuracy error converges to a lesser prediction error.
According to the Lyapunov method, the closed-loop adaptive control system is proven to be globally stable, with tracking errors converging to a neighborhood of zero.
By theoretical analysis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded, with tracking errors converging to zero.
Standard errors converge to the full analysis standard error of 0.063 for all effect sizes as n X approaches n Y.
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