### Table 4. Fuzzy systems of nonlinear plant.

"... In PAGE 9: ... The interpretability-driven simplification methods and the multi-objective genetic algorithm are used to optimize the initial fuzzy system. The performance of the obtained four Pareto-optimal fuzzy systems is described in Table4 . The decision-marker can choose an appropriate fuzzy system according to a specific situation, either the one with higher interpretability (less number of fuzzy rules or/and fuzzy sets) or the one with less error.... In PAGE 9: ... The decision-marker can choose an appropriate fuzzy system according to a specific situation, either the one with higher interpretability (less number of fuzzy rules or/and fuzzy sets) or the one with less error. Table4 also shows the comparison with other published results, which indicates that the proposed -2 -1.5 -1 -0.... ..."

### Table 2: Fuzzy sets in the universe of fairy-tale characters

"... In PAGE 9: ... Example 3.1 Table2 shows the membership degrees of the fuzzy sets beautiful, average and ugly in the universe X of 6 fairytale characters, while Table 3 de nes a resemblance relation R on X. For more details on the construction of R we refer to [7].... ..."

### Table 2. Fuzzy systems of nonlinear plant.

### TABLE I Fuzzy model of the nonlinear dynamic plant.

### Table 5: Fuzzy and neuro-fuzzy software systems.

2003

"... In PAGE 22: ...upports independent rules (i.e., changes in one rule do not effect the result of other rules). FSs and NNs differ mainly on the way they map inputs to outputs, the way they store information or make inference steps. Table5 lists the most popular software and hardware tools based on FSs as well as on merged FSs and NNs methodologies. Neuro-Fuzzy Systems (NFS) form a special category of systems that emerged from the integration of Fuzzy Systems and Neural Networks [65].... ..."

Cited by 2

### Table 1: Fuzzy sets for the loading process above the system capacity, because it wants to recover the production rapidly. In those cases, the WIP grows leading to great inventory costs, since the production system can apos;t produce all the parts loaded. To avoid this situation the variable WIP is introduced in order to restrict the number of parts in processing. The fuzzy control algorithm has the following struc- ture:For each input and output variable a group of fuzzy sets are de ned covering totally the universe of dis- course (set of all possible values). Table 1 shows the fuzzy sets de ned for all the variables included in the loading process.

### Table 1. Fuzzy logic tools and products. (Source: Sammy Wong and Nelson Wong, Computer Science Dept., Chinese University of Hong Kong.)

1993

"... In PAGE 43: ...IO- Another well- known and commercially available system is FRIL,I4 which is Prolog-based and has a highly sophisticated system for the man- agement of uncertainty. Still another exam- ple is the Yamaichi Securities Fund, and there are many more (see Table1 on page Elkan also seems to suggest that expert systems that combine grades of member- ship using operators other than max and min are not valid examples of the use of fuzzy logic. This position is hard to under- stand since the use of t-norms, t-conorms, and other connectives is now a standard part of fuzzy logic.... ..."

Cited by 45

### Table 1. Fuzzy control rules

"... In PAGE 3: ... 4. Simulation Because the linguistic terms, such as positive big (PB), positive medium (PM), positive small (PS), zero (ZE), negative small (NS), negative medium (NM), negative big (NB), are used, there are 49 rules ( see Table1 ) in the system. Table 1.... ..."

### Table 2. Simulation results of fuzzy system. Fuzzy Inference System

"... In PAGE 5: ... First, the fuzzy system and the PNN are exploited for the comparison of the proposed model, which has high accuracy and superior generalization capabilities. Table2 provides simulation results of the fuzzy sys- tem only. Triangular and Gaussian MF, and conse- quent polynomials of fuzzy rules are considered as modeling options.... ..."

### Table 7: Fuzzy rule table for and .

"... In PAGE 57: ..., 1986). Fuzzy rules for adjusting of and are listed in Table7 , while fuzzy rules for are shown is Table 8. The universe of discourse of total training error is partitioned into Small, Medium, and Big, while the universe of discourse of change of error between two consecutive iterations is partitioned into Negative, Zero, and Positive.... In PAGE 58: ...Table7 , while the universe of discourse of total training time is partitioned into Short, Medium, and Long. Again, there are nine rules for adjustment of the steepness parameter of the activation function.... ..."