Fuzzy Logic Approach - Computational Intelligence in Time Series Forecasting - page 166

G G G G G G G G G G G G G G G G G

1

0.8

0.6

0.4

0.2

0

0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4

input1,x(t-18)

G G G G G G G G G G G G G G G G G

1

0.8

0.6

0.4

0.2

0

0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4

output1,x(t+6)

Figure 4.4(a). Input and output domains partitioning by 17 GMFs for rule generation

mcgtry0.m: f uzzy-forecast of Mackey-G l ass-Series

1.5

1

0.5

0

550 600 650 700 750 800 850 900 950 1000

time(Second)-(nMF=17 & Training Data =1 to 500)

mcgtry 0 .m: fuzzy pre d iction errors f or Mackey-G l ass Chaotic S eries)

0.2

0.15

0.1

0.05

0

-0.05

-0.1

-0.15

-0.2

550 600 650 700 750 800 850 900 950 1000

time(Second)-(nMF=17 & Training Data= 1 to 500)

Figure 4.4(b). Forecasting chaotic series with fuzzy predictor with n = 27 GMFs