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Sufficient conditions for a stabilizing controller design are developed by using Lyapunov-based methodologies and Linear Matrix Inequality (LMIs) techniques.
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Statistical analysis on the impact of the various formulations and process on the particle properties was performed using response surface methodology, and linear and nonlinear ensemble models.
We will be able to link the total score of the full scale PANSS, with very little bias, to scores on the "Mini PANSS" using a summed-score IRT based methodology and linear interpolation.
Applying response surface methodology and multiple linear regressions enabled us to build phenomenological models relating PI to water and salt content, and to temperature.
The effects of operating parameters such as applied current intensity, electrolysis time and initial benzoic acid concentration on the degradation and mineralization efficiency were investigated with the application of factorial design methodology and simple linear models describing and predicting adequately the removal of the substrate and DOC were developed.
By means of different methodologies and resorting to linear programming, an efficient network design has been achieved that reduces the hydrogen utilities usage by 30% (achieving 30% more gas to the fuel system), while also dealing with the complexity and need for flexibility so characteristic of a refinery as well as the reduction of operating costs related to the pneumatic system.
In the end, the discussions for the methodologies of MAM, CDM and linear FEM were executed to compare the errors of predicting ultimate load.
Inter-story drift demands were calculated using different non-linear static methodologies and compared with results from non-linear dynamic analyses to identify the most appropriate simplified methodology for seismic performance evaluation.
Both methodologies (linear and nonlinear) are applied and results in terms of laminar speed and burned gas Markstein length are compared.
The differences observed while comparing the optimization results along with the response surface (RS) plots derived by RSM with those provided by ANN models, reflected the advantages and disadvantages of both the methodologies, and essentially the non-linear character of the PEUF process.
The high-complexity nonlinear diabetic patient Sorensen-model was considered and Linear Parameter Varying methodology was used to develop open loop model and robust controller.
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