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The architecture, called Time-delay Added Evolutionary Forecasting (TAEF), consists of two steps: parameters optimization and phase adjustment.
The presented procedures involve microcalorimeter measurement and calibration steps, parameters computation and systematic errors correction for both long-term and accelerated algorithms.
Critical parameters identified and monitored at pertinent junctures of the process as part of a control strategy allow for a shift in the focus of quality assessment from end product testing to the testing of surrogate process steps, parameters, and inputs further upstream that have been demonstrated to be directly linked with product performance.
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The proposed method is mainly comprised of two steps: parameter iteration and solution iteration.
As shown in Fig. 2, this algorithm consists of two major steps: parameter estimation and feature extraction.
The step measure parameter defines the boundaries between categories which should increase monotonically with categories.
Table 2 shows the values obtained for the step parameters in different scenarios.
The different curves belong to different time-step parameters; η = 2−3, 2−5, 2−7 up to 2−13.
Moreover, a detailed method has been designed to search the suitable quantization step parameters.
In a first step, parameters lumping distance and material constants were estimated by matching a theoretical prediction to experimental data.
In each given step, parameters were randomly chosen to be adjusted with probability pperturb.
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