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By using the previous reference point, the right core of the con- clusion calculated as

Figure 8 By using the previous reference point, the right core of the con- clusion calculated as

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Fig. 1. Abnormal conclusion generated by the KH technique.
Fig. 1. Abnormal conclusion generated by the KH technique.
Fig. 3. Location of involved fuzzy sets.
Fig. 3. Location of involved fuzzy sets.
of points and by averaging the various solutions in a weighted way. The overall solutions are More details on this technique can be found in [4]. It was shown in [40] that the generalized form of KH interpolator is a mathe- matically stable technique, i.e., independently of the location of the fuzzy sets being the basis of the calculation, the KH inter- polator is able to approximate “well” any continuous function; i.e., KH interpolator possesses a practically enhanced version of the general universal approximator property.
of points and by averaging the various solutions in a weighted way. The overall solutions are More details on this technique can be found in [4]. It was shown in [40] that the generalized form of KH interpolator is a mathe- matically stable technique, i.e., independently of the location of the fuzzy sets being the basis of the calculation, the KH inter- polator is able to approximate “well” any continuous function; i.e., KH interpolator possesses a practically enhanced version of the general universal approximator property.
The right flank support point is defined by the expression, The right core point for trapezoidal membership by using the Euclidean distance on all input spaces. For & input dimensions, the following hold. re . we
The right flank support point is defined by the expression, The right core point for trapezoidal membership by using the Euclidean distance on all input spaces. For & input dimensions, the following hold. re . we
With the reference point the left and right cores can be calcu- lated. For the right core The left flank support and left core points can be calculated in a similar way. C. IMUL Fuzzy Rule Interpolation for Multidimensional Input Spaces B. MACI Fuzzy Rule Interpolation for Multidimensional Input Spaces This technique incorporates certain features of the MACI and the conservation of fuzziness technique, which is also based on FERI in the original KH interpolation sense [30]. IMUL uses the vector representation form of fuzzy sets (first proposed in [41]), and it applies the coordinate transformation features of MACT. At the same time, it can take the fuzziness of the fuzzy sets in the input space at the conclusion as those are presented in the conservation of fuzziness technique. The advantage of this fuzzy interpolation technique is not only that it takes the fuzziness of the sets at the input spaces, but also takes the core at the consequents into the calculation. MACI works with the vector description of fuzzy sets. The fuzzy set A is represented by a vector a = [G_-m,-++,0,-++,4n], where az, (k € [—m,n]) are the char- acteristic points of A, and ag is the reference point of A with membership degree one. This means that az, = [a_m,..-, Go], and ar = [do,..-,@n] are the left flank and right flank of A, respectively. Similarly as in the case of KH interpolation, the basic technique of MACTI is extended to multidimensional input spaces using Euclidean distance on all input spaces. In our case, the reference points of all membership functions can be calculated by taking the mid point of the membership function. For & input dimensions, the reference point of the interpolated conclusion for trapezoidal membership function is
With the reference point the left and right cores can be calcu- lated. For the right core The left flank support and left core points can be calculated in a similar way. C. IMUL Fuzzy Rule Interpolation for Multidimensional Input Spaces B. MACI Fuzzy Rule Interpolation for Multidimensional Input Spaces This technique incorporates certain features of the MACI and the conservation of fuzziness technique, which is also based on FERI in the original KH interpolation sense [30]. IMUL uses the vector representation form of fuzzy sets (first proposed in [41]), and it applies the coordinate transformation features of MACT. At the same time, it can take the fuzziness of the fuzzy sets in the input space at the conclusion as those are presented in the conservation of fuzziness technique. The advantage of this fuzzy interpolation technique is not only that it takes the fuzziness of the sets at the input spaces, but also takes the core at the consequents into the calculation. MACI works with the vector description of fuzzy sets. The fuzzy set A is represented by a vector a = [G_-m,-++,0,-++,4n], where az, (k € [—m,n]) are the char- acteristic points of A, and ag is the reference point of A with membership degree one. This means that az, = [a_m,..-, Go], and ar = [do,..-,@n] are the left flank and right flank of A, respectively. Similarly as in the case of KH interpolation, the basic technique of MACTI is extended to multidimensional input spaces using Euclidean distance on all input spaces. In our case, the reference points of all membership functions can be calculated by taking the mid point of the membership function. For & input dimensions, the reference point of the interpolated conclusion for trapezoidal membership function is
Fig. 7. Undesirable feature of KH interpolation.
Fig. 7. Undesirable feature of KH interpolation.
Fig. 8. Broad interpolated fuzzy memberships.
Fig. 8. Broad interpolated fuzzy memberships.
COMPARISON RESULTS ON PETROLEUM DATA
COMPARISON RESULTS ON PETROLEUM DATA
Fig. 9. Comparison of KH, MACI, and IMUL on petroleum data.
Fig. 9. Comparison of KH, MACI, and IMUL on petroleum data.
COMPARISON RESULTS ON HYDROCYCLONE DATA
COMPARISON RESULTS ON HYDROCYCLONE DATA
Related topics:
MathematicsApplied MathematicsComputer ScienceFuzzy Rule InterpolationFuzzy Rule based SystemElectrical and Electronic EngineeringFuzzy Rule Base
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