### Table 1: Distribution of npform Feature on Markables (w/o 1st and 2nd Persons)

2003

"... In PAGE 2: ... If an expression is used to re- fer to an entity that is not referred to by any other expression, it is considered a singleton. Table1 gives the distribution of the npform at- tribute for NP-markables. The second and third row give the number of non-singletons and singletons re- spectively that add up to the total number given in the first row.... ..."

Cited by 18

### 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 3. Sample Run of the HABclass Network Tool

1994

"... In PAGE 10: ... Three of the non-singleton categories, categories B0, B1, and B5, were singled out as \interesting, quot; due in part to the large number of individual sequences which ended up in these categories. The sequences from these three cate- gories were run through ART again with = 0:99: Table3 shows the set of categories that resulted from this complete run of HABclass. 2 Comparing this to \ at quot; networks coming from application of ART with vigi- lance set at 0.... ..."

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### Table 2: The control function G.

"... In PAGE 7: ... The function G is set-valued, where a non-singleton set for a range value indicates a choice of next server positions. The control function is given in Table2 . We choose a control function whose range values do not depend on the current server position.... In PAGE 9: ...of G if there is a set of options. The rst two lines of Table2 indicate that the server must move to Tank 2 if either the liquid level of Tank 2 is about to drop below the pseudo lower bound ( i.e.... In PAGE 9: ...A requirement in the de nition of the control function is that the graph of G (restricted to the desired region of operation, A) must be closed. The control function G de ned in Table2 is not closed; however, the modi ed de nition G0 given in Table 3 is closed. Veri cation of closure of G0 is given in the Appendix.... In PAGE 18: ... (A formal de nition of closure is given in the Appendix.) By inspection, the function G given in Table2 is not closed. If a limit point is added by altering an inequality in one of the state conditions, the mapping G may no longer be a function.... In PAGE 18: ... For example, the set of points where x1 = h1, x2 lt; h2 and h3 x3 are limit points of G that are not contained in the graph of G. If we alter the eighth set of state conditions in Table2 (which corresponds to U0 8) by changing the inequality x1 lt; h1 to x1 h1, then G is no longer a function since the points in h1 lt; x1, x2 = h2 and x3 = h3 are contained in both the fth and the eighth sets of state conditions and, therefore, map to both f1g and f1; 2g. In our case, this required successively updating the control mapping by including limit points not originally contained in the graph while ensuring that the mapping was still a function.... ..."

### Table 5: Performance in the Groups: Supplier Sample (n=218)

### 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].... ..."

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

### Table 2: Number of controller refreshments in nonlinear simulations.

1997

"... In PAGE 11: ...: dotted every 50 deg.: dashdot; no refreshment: dashed The simulations corresponding to di erent refreshment strategies, Table2 , are compared in Figure 3 with the fully-continuous simulations, that are obtained using the original continuous gain-scheduled con- troller. It can be seen that when the discrete controller is refreshed at each sample, the time responses are... In PAGE 12: ...using the trapezoidal method with a sampling frequency of 50 Hz. When the discrete controller is refreshed every 30 degrees of observed deviation the total amount of ops is roughly divided by 120 ( Table2 ) without signi cant performance degradation. With fewer refreshments (every 50 degrees), alterations of the per- formance are more visible in the transient and steady-state zones of the responses.... ..."

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### Table 2: Number of controller refreshments in nonlinear simulations.

1997

"... In PAGE 11: ...: dotted every 50 deg.: dashdot; no refreshment: dashed The simulations corresponding to di erent refreshment strategies, Table2 , are compared in Figure 3 with the fully-continuous simulations, that are obtained using the original continuous gain-scheduled con- troller. It can be seen that when the discrete controller is refreshed at each sample, the time responses are... In PAGE 12: ...using the trapezoidal method with a sampling frequency of 50 Hz. When the discrete controller is refreshed every 30 degrees of observed deviation the total amount of ops is roughly divided by 120 ( Table2 ) without signi cant performance degradation. With fewer refreshments (every 50 degrees), alterations of the per- formance are more visible in the transient and steady-state zones of the responses.... ..."

Cited by 2

### Table 1: Glioma dataset: the median of similarity indices, computed for a34 a84 a39a223a46a21a64a2a64 trials when the hypothesized number of clusters varies between a14 and a224 . Bold characters are used to represent the maximum computed value for each similarity index, and for each clustering method. We estimate for all clustering algorithms and all similarity indices a83 a67

2003

"... In PAGE 2: ... For every algorithm a10a12a11 , the ex- periment is repeated a34 a84 a39a121a46a21a64a82a64 times, and the median for every considered similarity index is computed. Observe in Table1 that for a9 a39a123a120 non-singleton clusters the agreement between the ref- erence dendrogram and 23 samples-based dendrogram is perfect for all similarity indices and all clustering algorithms. Observe also that median value varies with a9 strongly dependent on the used clustering algorithm.... In PAGE 2: ... Observe also that median value varies with a9 strongly dependent on the used clustering algorithm. Based on cluster stability criterion, one can easily decide from Table1 that the number of distinct clusters present in the glioma dataset is a83 a67 a39a65a120 , which is in good agree- ment with the known pathological classification of that data set. Table 2 shows how the samples are assigned to the clusters when cutting the reference dendrogram at level a83 a67 a39a99a120 .... ..."

Cited by 3