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Table 4.1 The parameters used for generating fuzzy rules. See text for explanation
Fuzzy subspace ij | Membership grade (μ1, μ2) | β1 and β2 by operator | Class cij | Rule strength wij |
i=1, j=1 | Class 1=(0.3, 0.2) | β1=0.2 | c11=2 | w11=(0.5 − 0.2)/(0.5 +0.2)=0.43 |
| Class 2=(0.5, 0.6) | β2=0.5 |
| |
i=1, j=2 | Class 1=(0.3, 0.8) | β1=0.3 | c12=2 | w12=(0.4–0.3)/(0.4 +0.3) =0.14 |
| Class 2=(0.5, 0.4) | β2=0.4 |
| |
i=2, j=l | Class 1=(0.7, 0.2) | β1=0.2 | c21=2 | w2l=(0.5–0.2)/(0.5 +0.2)=0.43 |
| Class 2=(0.5, 0.6) | β2=0.5 |
| |
i=2, j=2 | Class 1=(0.7, 0.8) | β1=0.7 | c22=1 | w22=(0.7−0.4)/(0.7 +0.4)=0.27 |
| Class 2=(0.5, 0.4) | β2=0.4 |
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pixel is input to the fuzzy rule base, we first calculate the membership grades in all fuzzy subspaces for the pixel. The resulting membership grades are then combined with the rule strength as shown in step (1) of Figure 4.13b and the membership grade of an unknown pixel with respect to class x is represented by αx (refer to step (1)). After the parameter αx has been determined for each class x, we then allocate the pixel to the class c if αc is the maximal, as shown in Figure 4.13b, step (2). For instance, if the unknown pixel takes values (0.6, 0.9) on the two input features, then according to Figure 4.13b, step (1) (using the minimum operator ), we obtain
4.40 |
where μi, i {1, 2} is computed using Equation (4.38). This unknown pixel is placed in class 1 because α1>α2 (Figure 4.13b, step (2)).
The procedures shown in Figure 4.13 can be extended to deal with higher input dimensions. However, two points should be noted. In Figure 4.13a, step (2), if several classes take the maximum value or βx=0, for class x, the rule for the corresponding fuzzy subspace cannot be generated (one may adopt different training pixels to resolve such a problem). Similarly, in Figure 4.13b, step (2), if two or more classes take the maximum value or all αx are zero, the input pattern will be treated as unclassified.
The strength wij specified in Figure 4.13a is based on the following intuitive property. If all of the training patterns within a fuzzy subspace belong to the same class, the wij will then reach the maximum permitted value,
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