### Table 1: Fuzzy DL semantics.

"... In PAGE 3: ... A fuzzy interpretation I = ( I; I) relative to a fuzzy data type theory D = h D; Di con- sists of a nonempty set I (called the domain), disjoint from D, and of a fuzzy interpretation function I that co- incides with D on every data value, data type, and fuzzy data type predicate, and it assigns (i) to each abstract con- cept C a function CI : I ! [0; 1]; (ii) to each abstract role R a function RI : I I ! [0; 1]; (iii) to each abstract feature r a partial function rI : I I ! [0; 1] such that for all u 2 I there is an unique w 2 I on which rI(u; w) is defined; (iv) to each concrete role T a function RI : I D ! [0; 1]; (v) to each concrete fea- ture t a partial function tI : I D ! [0; 1] such that for all u 2 I there is an unique o 2 D on which tI(u; o) is defined; (vi) to each modifier m the modifier function fm : [0; 1] ! [0; 1]; (vi) to each abstract individual a an element in I; (vii) to each concrete individual c an ele- ment in D. The mapping I is extended to roles and complex concepts as specified in the Table1 (where x; y 2 I and v 2 D). We comment briefly some points.... ..."

### Table 2. Neuro-fuzzy system answers

"... In PAGE 2: ...ig. 1. Voltage peaks within 10 min. periods (maximal values). Experiment results are shown in Table2 . The neuro-fuzzy system was trained with 2000 training vectors consisting of 5 random values describing the disturbances within a 10 min.... In PAGE 2: ... The lower the output value the smaller the probability of a malfunction. Test input vectors and respective outputs are summarized in Table2 . In case 2 swells were severe, so the system output is close to 1.... ..."

### Table 4. Neuro-fuzzy system answers

"... In PAGE 3: ... On the contrary, if THD is high and H19, H21, overvoltages are allowed - normal operation is expected. Investigation results are shown in Table4 . As before, most cases were correctly recognized, accordingly to the learning patterns (Table 4, cases 1.... In PAGE 3: ... Investigation results are shown in Table 4. As before, most cases were correctly recognized, accordingly to the learning patterns ( Table4 , cases 1.... ..."

### Table 7 below summarizes the results of comparison of MMR with K- modes, fuzzy K- modes and fuzzy centroid.

"... In PAGE 13: ...Table 7 below summarizes the results of comparison of MMR with K- modes, fuzzy K- modes and fuzzy centroid. Table7 : Comparison of MMR with algorithms based on Fuzzy Set Theory Data Set K-modes Fuzzy K-modes Fuzzy Centroids MMR ZOO 0.60 0.... ..."

### Table 2: Classification Scheme for Fuzzy Set Research in Production Management

1998

"... In PAGE 3: ... The conclusions to this study are given in Section 4. 2 Classification Scheme for Fuzzy Set Theory Application in Production Manage- ment Research Table2 illustrates a classification scheme for the literature on the application of fuzzy set theory in production management research. Seven major categories are defined and the frequency of citations in each category is identified.... ..."

Cited by 3

### Table 2: The fuzzy confirmability of for

"... In PAGE 26: ...Example 2. Fuzzy abduction with trick. Assume the set of diseases D = , S = . The fuzzy confirmability of for , is given as a fuzzy relation, listed in Table2 . The observed symptoms are denoted as a fuzzy set in S, and the corresponding certainty membership of the observed symptoms belonging to , , are listed as a vector ( ).... ..."

### Table 1 Fuzzy rules

"... In PAGE 9: ... Based on the assumption of input and output parameters, the fuzzy rule FR in our fuzzy logic controller is IF MinSupport is A and Lean is B THEN RealSupport is C where A, B and C are fuzzy sets. The following Table1 is an example for illustrating the construction of fuzzy rules. In Table 1, the first column is the fuzzy sets in F Lean; the first row is the fuzzy sets in F MinSupport; and others are the outputs generated for Real- Support.... In PAGE 9: ... The following Table 1 is an example for illustrating the construction of fuzzy rules. In Table1 , the first column is the fuzzy sets in F Lean; the first row is the fuzzy sets in F MinSupport; and others are the outputs generated for Real- Support. Each output is a fuzzy rule.... ..."

### Table 3. Fuzzy Values

1997

"... In PAGE 3: ... Some analysis was then performed on these metrics. Secondly, the programs were presented to two experienced software developers for subjective analysis, and fuzzy logic metrics (Table 2) were derived using the best match of the labels in Table3 . After analysis of these fuzzy metrics for author discrimination, the metrics were combined to see if a synergy of the two forms could achieve better results.... ..."

Cited by 6

### Table 4 Section First fuzzy set Second fuzzy set

"... In PAGE 5: ...e. Ind2=Ind1+1={b7,b6,b5}+1 as expressed in Table4 below. Table 4 Section First fuzzy set Second fuzzy set... ..."