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This chapter provides a review of methods of parameter sensitivity analysis and considers applications involving linear and nonlinear lumped parameter models.
After that, two general methods of parameter estimation were applied to analyze the nanoindentation data and global optimal results were sought by multiple runs with the random starting values.
Calculation of sensitivities of stochastic chemical systems using Kinetic Monte Carlo and finite-difference-based methods is not only computationally intensive, but direct calculation of sensitivities by finite-difference-based methods of parameter perturbations converges very poorly.
However, these models usually come at high computational costs, which limit their accessibility to state-of-the-art methods of parameter estimation and uncertainty quantification, because these methods require a large number of model evaluations.
To improve genetic algorithm convergence, efficient methods of parameter optimization were suggested and compared.
For the calculation of β, in [15] and [16], the calculation methods of parameter β are based on overall SNR of each frame, and a linear relationship between β and frame SNR was applied.
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CO hydrogenation over supported Rh catalysts was studied in an automatized microreactor by methods of parameter-programmed reaction.
Estimation methods of parameters were presented.
Figure 7 Comparison of three methods of parameters estimation with SNR = −6 dB.
The simulation comparison result of W V peak detection method and the normalized first moment method are shown in Figure 6. Figure 6 Comparison of three methods of parameters estimation.
The methods of parameters' measurement are seen in Figure 1.
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