Recent Contributions in Intelligent Systems by Vassil Sgurev Ronald R. Yager Janusz Kacprzyk & Krassimir T. Atanassov

Author:Vassil Sgurev, Ronald R. Yager, Janusz Kacprzyk & Krassimir T. Atanassov
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

SGA_SCM

SGA_MCS

Before PMPG

After PMPG

Before PMPG

After PMPG

J

0.0225

0.0222

0.0225

0.0222

time, s

25.98

18.45

25.88

19.66

μ 2S

0.96

0.95

0.92

0.91

μ 2E

0.11

0.12

0.11

0.12

k S

0.13

0.12

0.12

0.12

k E

0.75

0.79

0.77

0.79

Y SX

0.41

0.4

0.43

0.41

Y EX

1.48

1.67

1.45

1.57

79.31

94.03

70.33

81.71

Y OS

627.78

729.57

526.3

650.34

Y OE

187.09

192.43

140.25

138.08

As expected and shown in the previous subsection as well, the application of the purposeful model genesis procedure for both SGA considered here leads to expecting decrease of the convergence time. Meanwhile, even slight improvement of the model accuracy has been observed. In comparison to the results before the procedure application, up to 24 % reduction of the computation time of SGA-MCS without loss of model accuracy has been achieved, while for SGA-SCM even 29 % of computation time has been saved, thus showing good effectiveness of PMPG.

Table 10 lists the estimations assigned to each of the estimated parameters concerning Table 8 for the considered SGA_SCM nd SGA_MSC in “broad” and “narrow” ranges.Table 10Models parameter estimations before and after PMPG

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.