274 CHAPTER 8. APPLICATIONS
Initial MF's on Area
0
0.2
0.4
0.6
0.8
1
1.2
0 1000 2000 3000 4000 5000
Area
Final MF's on Area
0
0.2
0.4
0.6
0.8
1
1.2
0 1000 2000 3000 4000 5000
Area
Figure 8.15. Initial andfinal membership functions onarea: 232-partition
Initial MF's on Solidity
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1
Solidity
Final MF's on Solidity
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1
Solidity
Figure 8.16. Initial andfinal membership functions onsolidity: 232-partition
Initial MF's on Edif
0
0.2
0.4
0.6
0.8
1
1.2
0 0.1 0.2 0.3 0.4 0.5
Edif
Final MF's on Edif
0
0.2
0.4
0.6
0.8
1
1.2
0 0.1 0.2 0.3 0.4 0.5
Edif
Figure 8.17. Initial andfinal membership functions onEdif: 232-partition
The classification results of the training data are illustrated in Table 8.9.
The classification results indicated superior classification rates with ANFIS com-
pared to the fuzzy clustering and the backpropagation neural network. A total
of 5 trash objects out of 213 trash objects were misclassified resulting in a clas-
sification rate of 97.653%.
Table 8.9. Classification results of trash objects
in the training data using ANFIS: 232-partition
Total number Actual Classified trash type
of objects trash type Bark Stick Leaf Pepper
18 Bark 16 2 0 0
34 Stick 0 33 1 0
54 Leaf, 0 1 53 0
107 Pepper 0 0 1 106