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A new method for tuning the parameters of the proportional integral derivative (PID) controller is presented in this paper.
Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration.
In this paper, an analytical method for tuning the parameters of the set-point weighted fractional order PID (SWFOPID) controller is proposed.
The convergence conditions together with the performance measure jointly constitute a design tool for tuning the parameters of the variable-gain controller.
Hence, the presented approach enables a simple, computationally tractable and efficient constrained optimization solution for tuning the parameters of the controller, while considering the eventual gain, pole, zero and time-delay uncertainties defined using an interval model of the controlled process.
Furthermore, Niaki and Hoseinzade (2013) used ANN for forecasting S&P indices where the experimental design was used for tuning the parameters of the neural network.
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In this paper, a simple analytical method is proposed for tuning the parameters of these controllers.
For tuning the parameters of algorithms, extensive experiments is conducted with different sets of parameters with respect to Taguchi design.
An ANN is used for self-tuning the parameters of PSS in real-time.
They used simulated annealing (SA) for tuning the best parameters of fuzzy systems.
On the downside, it requires tuning the parameters for the GA and selection of a proper fitness function, whereas or method is tunable by a single parameter.
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