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First, we present a robust adaptive update law that allows us to estimate time-varying parameters by using the fuzzy basis function expansion.
This is very important because it allows us to easily determine kinetic parameters by using the method of initial rates (vide infra).
We estimate the parameters by using the maximum likelihood method.
However, it is also possible to set the parameters by using fictive information.
Select the most significant parameters by using the single variable procedure.
Then, the process to determine optimal geometry parameters by using performance atlases is presented.
The calculated values of these parameters by using the correlations were compared with the experimental data.
Our results show that we can obtain accurate FF parameters by using this technique.
Openness to different data formats regarding input and output parameters by using JSON as data exchange format.
Hereafter, optimize the model of LSSVM to find out the optimum value of parameters by using training data and MFOA.
Firstly, we propose the local estimations of drift time-varying parameters by using the local weighted least squares method.
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