### 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 1 An a priori fuzzy rule base

"... In PAGE 15: ...2 Fuzzy control using an a priori fuzzy model Because the rocket velocity will increase as the velocity of the exhaust gases increases, a rule base can be constructed a priori based only on logical consid- erations. Table1 shows this three-dimensional rule base as a sequence of two- dimensional tables, and for each of them variable C takes a different value. This rule base can be used as a starting point in the control/identification process, so that the modifications required in the learning process are applied over it.... ..."

### Table 3: Fuzzy knowledge base rule for eachjoint

"... In PAGE 5: ... Table 2 gives the controller parameters such as PD gains, cubic feedback gains, fuzzy control gains and = diagf 1 ;; 2 g. During the simulation, the decentralized fuzzy control for each joint used a fuzzy rule shown in Table3 . That is, for eachoftwo inputs e i and _ e i , 7 fuzzy terms were used.... ..."

### TABLE III TEST ERROR OF DIFFERENT FUZZY RULE-BASED CLASSIFIERS AFTER PERFORMING A FEATURE SELECTION, WITH THE ORIGINAL MIFS ALGORITHM AND WITH THE MODIFIED VERSION PROPOSED IN THIS PAPER. THE NUMBER OF FEATURES SELECTED IS SHOWN IN THE FIRST COLUMN.

in Some Results about Mutual Information-based Feature Selection and Fuzzy Discretization of Vague Data

### Table 2. Number of selected features and derived rules for each predicted output.

"... In PAGE 6: ... All the fuzzy rule bases obtained adopting the MISO approach without feature selection present 8 rules with 32 input variables. In Table2 are reported the rule and input numbers for each of the fuzzy model generated with the implementation of the feature selection procedure. 6 Conclusions The proposed modeling framework provides an effective tool for automatically... ..."

### Table 1. A typical rule base for a fuzzy PD controller (25 rules).

2005

"... In PAGE 3: ... Fuzzy inference is used to compute the control signal u. Table1 shows a typical rule base for a fuzzy PD controller with 25 rules. Five linguistic terms are used for each variable, negative big (NB), negative small (NS), zero (ZE), positive small (PS), and positive big (PB).... ..."

### Table 1 Fuzzy rule bases

2006

"... In PAGE 3: ... The rules reflect an initial strategy for combining the different forecast values that has been suggested by a user. For example, if the same level of trust is given to the customer forecast and expert forecast, the rules in Rule Base 1 can have the form as given in Table1 (a). Rule Base 2 and Rule Base 3 are defined in a similar way (see Table 1 (a) and (b), respectively).... In PAGE 3: ... For example, if the same level of trust is given to the customer forecast and expert forecast, the rules in Rule Base 1 can have the form as given in Table 1 (a). Rule Base 2 and Rule Base 3 are defined in a similar way (see Table1 (a) and (b), respectively). However, the proposed DSS_DF includes a learning mechanism that modifies and improves the initial rule bases... ..."

### Table 2: The rule base of a fuzzy controller. small medium big huge

1994

"... In PAGE 15: ... The fuzzy partitions of X1; X2; Y are shown in gures 8 { 10, respectively. Table2 shows the rule base of the fuzzy controller. From the fuzzy partitions we can derive the following scaling functions.... ..."

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