Based on modeling and error analysis, stochastic error of inertial sensor, measurement noise, modeling error and environmental disturbance are formulated into different types of disturbances described by a Markov stochastic process, Gaussian noise and a norm-bounded variable, respectively.
An analytical method for the simultaneous detection of MCP and DDVP was constructed by establishing 24 linear regression models and error analysis was performed to verify the practicability of these models.
The model comparisons and error analysis reveal that the application of artificial intelligence models can be more effectively applied to brittleness prediction compared with simple regression correlations.
The superiority of the proposed modified model was shown by comparing it with the Alg model as well as with a widely adopted modified Bouc Wen model through an error analysis.
A philosophy for electrochemical impedance spectroscopy is presented which integrates experimental observation, model development, and error analysis.
The model is calibrated to the experiments and the confidence intervals for the estimated parameters are used to account for the model error throughout the analysis.
We can see that the mixed model of this paper is superior to each sub-model from the error analysis.
In summary, the results from four Bayesian models are compared: a simple model under the naive analysis (N-S), an adjusted model by confounders under a naive analysis (N-A), a simple model under a measurement error analysis (ME-S), and an adjusted model by confounders under a measurement error analysis (ME-A).
Comparative analysis and non linear optimization of nine isotherm models through comprehensive error analysis revealed that Flory–Huggins and Redlich Peterson equations best described the adsorption.
This approach is differentiated from the usual sequential model development for given impedance spectra by its emphasis on obtaining supporting observations to guide model selection, use of error analysis to guide regression strategies and experimental design, and use of models to guide selection of new experiments.
