The identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory.
To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors.
After the identification of a model of preliminary transfer function, as shown in Equation (24), it is necessary to estimate the general parameters δ = (δ1, …, δ r )', w = (w0, w1, …, wLs)', ϕ = (ϕ1, …, ϕ p )', θ = (θ1, …, θLq)', and σ a 2. In general, such estimates are carried out interactively with the help of computer programmes.
Consequently, the identification of a model, genetically tractable, monocot system that is more closely related to wheat is highly desirable.
One of the major points to decide upon was the identification of a model for primary care which could maximize access while minimizing the risk of overdose.
We then apply the EM algorithm to a real problem, the identification of a model for the Escherichia coli central carbon metabolism, based on challenging experimental data from the literature.
The major steps of this mathematical modeling process comprise model discrimination, i.e. identification of an appropriate model structure, model calibration, i.e. estimation of unknown model parameters, as well as prediction and model validation.
The model is obtained through linearization of a physical model, simplification using the cumulant matching method and analytic identification of a nonlinear model coinciding with the linear model around equilibrium points, corresponding to the hydraulic permanent regimes.
To overcome this problem, this study proposes the identification of a meta-model, i.e. a model of a simulation model, for describing an irrigation district.
This technique also allows for the identification of a best model and creates regression coefficients based on all the possible models (model-averaged coefficients), which are more robust than typical regression coefficients.
Establishment of proper mathematical representations requires identification of a suitable model with an adequate framework and structure to determine the parameters in the framework.
