Table 7 Optimized model parameters.

From: A fault diagnosis method for wireless sensor network nodes based on a belief rule base with adaptive attribute weights

Ruler number

Input attributes

Rule weight

Belief distribution

MeanGap

Kurtosis

{NS, OSF, HNF, OLF, FVF}

1

S

S

0.16930.3636

0.21816, 0.09817, 0.27579, 0.13844, 0.26944

2

S

RS

0.4936

0.0024, 0.40539, 0.01229, 0.03631, 0.54362

3

S

RL

0.9784

0.0623, 0.06429, 0.06813, 0.41309, 0.39219

4

S

L

0.9234

0.09458, 0.135282, 0.065474, 0.12399, 0.580676

5

RS

S

0.0062

0.0448, 0.517374, 0.00352, 0.000652, 0.433654

6

RS

RS

0.0118

0.99599, 0.00193, 0.00006, 0.00172, 0.00031

7

RS

RL

0.0028

0.36190, 0.36689, 0.25268, 0.00607, 0.01246

8

RS

L

0.0002

0.30607, 0.07011, 0.12829, 0.10386, 0.39168

9

M

S

0.8700

0.00050, 0.00036, 0.00189, 0.00124, 0.99601

10

M

RS

0.0001

0.63492, 0.05458, 0.09995, 0.15465, 0.05589

11

M

RL

0.0033

0.22425, 0.07192, 0.08152, 0.26334, 0.35897

12

M

L

0.0082

0.02146, 0.17697, 0.07801, 0.11676, 0.60680

13

RL

S

0.0094

0.88110, 0.07109, 0.04148, 0.00366, 0.00267

14

RL

RS

0.0723

0.45348, 0.30286, 0.03648, 0.01569, 0.19148

15

RL

RL

0.2681

0.47403, 0.19242, 0.21118, 0.0733, 0.04904

16

RL

L

0.0964

0.21497, 0.18619, 0.08708, 0.38059, 0.13118

17

L

S

0.7690

0.00430, 0.09177, 0.34379, 0.52273, 0.03741

18

L

RS

0.9867

0.00018, 0.56913, 0.11641, 0.03250, 0.28178

19

L

RL

0.9347

0.08987, 0.35595, 0.10905, 0.13588, 0.30925

20

L

L

0.3636

0.08792, 0.17882, 0.23044, 0.05537, 0.44745

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