Selected article for: "cost function and iteration number"
Author: Sina F. Ardabili; Amir MOSAVI; Pedram Ghamisi; Filip Ferdinand; Annamaria R. Varkonyi-Koczy; Uwe Reuter; Timon Rabczuk; Peter M. Atkinson
Title: COVID-19 Outbreak Prediction with Machine Learning
  • Document date: 2020_4_22
    • ID: nu0pn2q8_32
    Snippet: where t is represents repetition of the algorithm. and are vectors of the prey site and the vectors represent the locations of the grey wolves. is linearly reduced from 2 to 0 during the repetition. 1 ⃗⃗⃗ and 2 ⃗⃗⃗ are random vectors where each element can take on realizations in the range [0.1]. The GWO algorithm flowchart is shown in Figure 4 . In the present study, GWO [70] was employed for estimation of the parameters of Eq.1 to 8.....
    Document: where t is represents repetition of the algorithm. and are vectors of the prey site and the vectors represent the locations of the grey wolves. is linearly reduced from 2 to 0 during the repetition. 1 ⃗⃗⃗ and 2 ⃗⃗⃗ are random vectors where each element can take on realizations in the range [0.1]. The GWO algorithm flowchart is shown in Figure 4 . In the present study, GWO [70] was employed for estimation of the parameters of Eq.1 to 8. The population number was selected to be 500 and the iteration number was determined to be 1000 according to different trial and error processes to reduce the cost function value. The cost function was defined as the mean square error between the target and estimated values according to Eq. 14.

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