The problem of model establishment includes optimization of model parameters, kernel function selection, feature vector extraction, and unbalanced sample.
We consider the optimization of model parameters (including both dust model parameters and TCWV) through a least squares analysis.
The customization of the interfaced learners includes, for example, descriptor scaling, optimization of model hyper-parameters and appropriate stopping criteria.
The careful optimization of model parameters is necessary to strike a balance between simple and efficient computation and noise robustness.
In fact, ANN and ANFIS models need lots of input output data for optimization of model parameters which is not an easy task.
But for traditional multiple kernel learning (MKL), many classification machines including INMKMHKS focus on MKL optimizations, for example, the optimization of model for a MKL.
Recently, simulation-based optimization has become a popular and efficient tool in many domains (Banks [21], Hagendorf et al. [22] and Figueira et al. [23]).It involves the optimization of model inputs by using simulation for the computation of parameters.
Accurately modeling the PEM fuel cells as well as solar cells is critical in their applications, and this involves the identification and optimization of model parameters.
Future investigations can be carried out to improve and perfect the integrated model, such as the optimization of model parameters to better characterize the reactor/module network in the integrated ERN model.
The GP has the capability to carry out simultaneous optimization of model relationship structures and parameters, as well as to identify the most important basis functions.
