Table 2 FBQP model.(2) with (4.2)

From: Dynamic analysis of a fuzzy Bobwhite quail population model under g-division law

 

\(A_{r}\)

\(B_{r}\)

\(x_{-2,r}\)

\(x_{-1,r}\)

\(x_{0,r}\)

\(x_r\)

\(x^*_{r}\)

\(A_{l}\)

\(B_{l}\)

\(x_{-2,l}\)

\(x_{-1,l}\)

\(x_{0,l}\)

\(x_l\)

\(x^*_{l}\)

\(\alpha \)=0

2.0000

0.3500

1.2500

1.3500

1.4500

(2.0000,6.2819)

3.8192

 

1.5000

0.3366

0.7500

0.6500

0.5500

(1.5000,3.3997)

2.0728

\(\alpha \)=0.25

1.9665

0.2433

1.2165

1.3031

1.3897

(1.9665,6.0414)

4.6126

 

1.5335

0.1567

0.7835

0.6969

0.6103

(1.5335,3.5571)

2.1594

\(\alpha \)=0.5

1.9268

0.3207

1.1768

1.2475

1.3182

(1.9268,5.7677)

3.4860

 

1.5732

0.1793

0.8232

0.7525

0.6818

(1.5732,3.7487)

2.2664

\(\alpha \)=0.75

1.8750

0.3000

1.1250

1.1750

1.2250

(1.8750,5.4283)

3.2705

 

1.6250

0.2000

0.8750

0.8250

0.7750

(1.6250,4.0073)

2.4135

\(\alpha \)=1

1.7500

0.2500

1.0000

1.0000

1.0000

(1.7500,4.6779)

2.8081

 

2.0000

1.7500

1.0000

1.0000

1.0000

(1.7500,4.6779)

2.8081

  1. The fuzzy positive solution have a similar presentation as in Case I, it is bounded and persistent that \(x_n \in (x_{l,\alpha },x_{,\alpha })=(A_{l,\alpha },A_{r,\alpha }+ \frac{1+B_{l,\alpha }}{A_{l,\alpha }}+ \frac{1+B_{r,\alpha }}{A_{l,\alpha }^2A_{r,\alpha }}+\frac{1}{A_{l,\alpha }^3A_{r,\alpha }^2})\) as the following table shows.
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