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In SVR, the structural risk minimization is implemented for minimizing the upper bound of generalization error, while ANN takes the advantage of empirical risk minimization to minimize the prediction error (Asoodeh and Bagheripour 2013).
On the other hand, an adaptive feedforward algorithm which is in charge of the vibration minimization is implemented.
The total cost function minimization is implemented using genetic algorithms, which have the ability to calculate the global optimum in the overall state space with relative computational simplicity.
The cost (objective) function minimization is implemented using genetic algorithms, which, compared to conventional optimization methods such as dynamic programming and gradient techniques, have the ability to attain the global optimum with relative computational simplicity.
Cost function minimization is implemented as a repeated loop for each voltage vector to predict the active power, reactive power, and inductor current values, evaluate the cost function, and store the minimum value and the index value of the corresponding switching state for the respective switches.
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As shown in Fig. 7, the total line loss of the distribution system is reduced from 154.2 to 140.4 kW after the line loss minimization strategy is implemented.
The SVR method is based on the employment of the structural risk minimization principle; hence, it provides better generalization performance in comparison to the conventional Artificial Neural Networks (ANNs) in which the empirical risk minimization principle is implemented.
The proposed line loss minimization strategy is implemented at 2.0 s (the output of the OPF calculator is 0) and the security enhancement strategy is implemented at 10.0 s ((Delta dot{I}_{U}^) is generated by the OPF calculator to take corrective action).
The resulting Equivalent Consumption Minimization Strategy (ECMS) is implemented in real time and tested in simulation and in an experimental environment (HyHiL test bench).
The minimization loop based on the steepest descent technique is implemented within a line search strategy.
A mixed topology optimization algorithm is implemented and presented for the compliance minimization problems of continuum structures with material volume constraints.
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minimization is formulated
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minimization is based
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