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To decrease the computing gap the focus is on enhancing the information flow between humans and machines.
In order to decrease the computing time, the number of source nodes is reduced.
Clusterwise reduction is adopted to extract the most representative operating scenarios with PVs and BESS integration, which is able to decrease the computing complexity caused by scenario redundancy.
The main goal of the proposed method is to decrease the computing cost of original TLBO and extend it for optimizing the parameters of artificial neural network (ANN).
In order to compute the optimal parameters, the (rho (mathcal{L}_{gamma,omega})) is quite expensive and may greatly decrease the computing efficiency of the AOR iteration method from Theorem 2.1.
In order to compute the optimal parameters, the spectral radius of the corresponding SOR iteration matrix is required, which may greatly decrease the computing efficiently of the AOR iteration.
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Then, through transforming the robust H∞ problem of the uncertain LPV error system into a certain one, the tensor-product (TP) model transformation technique can be directly employed to obtain a convex polytopic form, which greatly decreases the computing load of the controller design.
Quantitatively, we calculated the spatial overlap between the identified networks and templates which are detailed in Tables 4 and 5. From these results, we can see that the integrated sampling module in HAFNI framework via HELPNI system significantly decreased the computing time while achieved comparable results for functional brain network identification at the same time.
One alternative solution to decreasing the computing time for BEAGLE and fastPHASE is parallel processing of the chromosome, as findhap implements a parallel computing algorithm.
The use of prior information on the sparsity of T-1 decreased the computing time for inversion by a factor of 1.73 on average.
Previous studies have mostly relied on parallelization and grid computing to decrease the computational time of MCSs.
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