### Table 2: Approximative Results

"... In PAGE 6: ... But previously, and because of the characteristics of this genetic algorithm, the mul- tidimensional fuzzy sets need to be projected in each domain and be approximated via a trapezoidal fuzzy set. Table2 shows the results when we consider 4 and 11 clusters and the nal results after the Genetic Al- gorithm is applied to the collection of fuzzy rules gen- erated from the fuzzy clusters: In the second approach: First, a combination of two fuzzy clustering al- gorithms is used: a substractive clustering is ap- plied to the product space of the input and out- put variable to generate the number of rules and a rst approximation to the fuzzy rules; then a classical fuzzy c-means algorithm is used to op- timise the fuzzy rules obtained. The paramet- ers considered in the substractive clustering al- gorithm were = 1 and = 2.... ..."

### Table 1: Approximation results

"... In PAGE 4: ...81 for the test set (see Fig. 2 a and Table1 .).... ..."

### Table 4: Comparison of Exact and Approximation results for ALL

1996

"... In PAGE 9: ...he average error is 7.2% and the standard deviation is 4.91. Similar results are tabulated in Table4 for AllBFs. The average error for DR is about 5.... ..."

Cited by 2

### Table 1: Table of known dense approximability results.

1997

Cited by 18

### Table 1: Table of known dense approximability results.

1997

Cited by 18

### Table 1: Table of known dense approximability results.

1997

Cited by 18

### Table 2: Approximation Results for Arti cial Examples

1994

Cited by 15

### Table 3: Comparison of Exact and Approximation results for METAL

1996

Cited by 2

### TABLE 1.1. Approximation Results

1997

Cited by 1