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A method for parameter estimation of two time-scale model of photosynthesis and photoinhibition is presented.
The proposed method uses the pure sinusoidal model in combination with the algebraic derivative method for parameter identification.
A model-based data mining method for parameter estimation is proposed as central block to classify the observed experimental conditions as compatible or incompatible with the approximated model.
In this paper, we study in depth a new modeling method for parameter curves and surface-hyperbolic polynomial uniform B-spline surfaces with shape parameter.
This paper introduces a freeware program for models analysis, which implements a robust method for parameter estimation of nonlinear models, able to detect outliers.
We also provide a new method for parameter matching during testing data interpretation through feature points׳ analysis, which facilitates the matching process and reduces ambiguity of parameters estimation.
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The curve fitting was carried out using the R statistical package version 3.2.5 using the "fitdistrplus" library with default maximum likelihood method for parameters estimation.
After completing the above specifications, the docking method and parameters for AutoDock are set.
Therefore, specialized numerical methods for parameter estimation in stochastic models have been developed.
The experimental and calculational methods for parameter d* estimation are analyzed.
In this work methods for parameter estimation in systems of nonlinear differential equations are compared.
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