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In this study, the Taguchi approach of parameter design was used as a statistical design of experiment technique to set the optimal welding parameters.
We propose a novel approach of parameter identification using the adaptive synchronized observer by introducing an auxiliary subsystem, and some sufficient conditions are given to guarantee the convergence of synchronization and parameter identification.
The next step to be investigated would be an online approach of parameter estimation and optimum experimental design (OED) for parameter identification of manipulators, which monitors the current precision and sequentially identifies itself using optimal measurement configurations calculated on-the-fly when necessary.
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Further, the performance of Kostiakov model has been found to be better than the Horton and Philip's model in most of the cases based on both the approaches of parameter estimation.
Using this approach, regions of parameter space that cannot describe the data can systematically be determined.
A systematic approach of controller parameter optimization is detailed.
However, nonlinear dynamic optimization approach to parameter estimation of biochemical networks has a number of limitations.
Optimization with Fuzzy Modeling Approach of PID parameters has been performed to maximize the performance of the system.
Support Vector Regression (SVR) is used to build the model of heat rate and the optimal selection approach of SVR parameters is discussed.
In this paper, we propose a novel approach of identifying parameters by the adaptive synchronized observer, and a filter in the output is introduced to suppress the influence of noise.
In the current study, a quantitative approach of NIRS parameters was used.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com