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This paper applied a novel global meta-heuristic optimization algorithm, cuckoo search algorithm (CSA) to determine optimal coefficients of a fractional delay-infinite impulse response (FD-IIR) filter and trying to meet the ideal frequency response characteristics.
In this paper, a new meta-heuristic optimization algorithm, called cuckoo search algorithm (CSA) is applied to determine the optimal coefficients of the finite impulse response-fractional order differentiator (FIR-FOD) problem.
A simple optimization using a "random walk" through parameter-space was used to determine the optimal coefficients compared to the measured S-values of 37 peptides.
Optimal coefficients of the transfer function of the controller are obtained by minimizing the cost function.
A hybrid genetic algorithm-pattern search tool was then employed to find the optimal coefficients of this formula.
Particular emphasis is put on the optimal coefficients highlighted recently in a theoretical study based on a normal mode stability analysis.
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In this paper, based on the LQR approach, a new optimal control structure is proposed, which optimizes the combined stochastic criterion that includes the identification of the optimal coefficient for adjusting the given trade-off.
Optimal coefficient selection (sparsing) is performed by truncation in magnitude.
In the other words, estimated value of effort is modified by determining the optimal coefficient.
The optimal coefficient of performance (COP), specific cooling power (SCP) and cooling capacity of this prototype are 0.197, 205.2 W kg−1 and 1.64 kW, respectively.
Moreover, for all the Fourier numbers considered, the numerical solution is stable and oscillation-free when the optimal coefficient of the model problem is used.
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