### Table 1. Comparison between fuzzy systems and neural networks

"... In PAGE 3: ... This means we are in a classical situation to apply a neural network. Consider Table1 : using a fuzzy system has obviously some benefits over using a neural network. We can interpret a fuzzy system as a system of linguistic rules.... ..."

### Table 5. Performance of neural networks

2005

"... In PAGE 4: ... The training set was broken up as 80% training and 20% cross validation. Table5 reveals the performance of backpropagation and conjugate gradient algorithm for the directional prediction of Microsoft stocks for different number of hidden neurons. Performance of the Mamdani Fuzzy Inference System (FIS) is illustrated in Table 6.... ..."

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### Table 5. Illustration of fuzzy control for the neural network

### Table 1 Neural network architectures

2003

"... In PAGE 6: ...etter as the scale is increased, i.e. as the data becomes smoother. On the final smooth trend curve, resid(t)in Table1 , a crude linear extrapolation estimate, i.e.... In PAGE 6: ...avelet coefficients at higher frequency levels (i.e. lower scales) provided some benefit for estimating variation at less high frequency levels. Table1 sum- marizes what we did, and the results obtained. DRNN is the dynamic recurrent neural network model used.... In PAGE 6: ...sed. The architecture is shown in Fig. 3. The memory order of this network is equivalent to applying a time- lagged vector of the same size as the memory order. Hence the window in Table1 is the equivalent lagged vector length. In Table 1, NMSE is normalized mean squared error, DVS is direction variation symmetry (see above), and DS is directional symmetry, i.... In PAGE 6: ... Hence the window in Table 1 is the equivalent lagged vector length. In Table1 , NMSE is normalized mean squared error, DVS is direction variation symmetry (see above), and DS is directional symmetry, i.e.... In PAGE 7: ...ion of these results can be found in Ref. [4]. For further work involving the DRNN neural network resolution scale. From Table1 , we saw how these windows were of effective length 10, 15, 20, and 25 in terms of inputs to be considered. Fig.... ..."

### Table 7 provides rough guidelines for applying neural networks to problems or

in Enhancing our Understanding of the Complexities of Education: "Knowledge Extraction from Data" using

"... In PAGE 22: ... (See Table 6) Table 6: Over and Under-representation of Asian/Pacific Island Students Group CHI FIL JAP KOR SEA PI SA WA ME OTH 1 -1% 3% -2% -4% 6% 4% -5% -1% -1% 1% 2 -1% 1% 4% -9% 5% 1% -1% -2% 4% -1% 3 0% 0% 1% 1% -3% -3% 3% 1% 1% -1% 4 9% -5% 1% 2% -7% 0% 0% -2% -2% 4% 5 -2% -3% -1% 6% -1% 0% 2% 0% -1% -1% Si milar discrepancies appear among Hispanic subgroups. Table7 suggests that the pattern of representation of the Hispanic aggregate group was substantially driven by the distribution of Mexican (MEX) students. Cuban students, to the contrary, were more likely to be found grouped with Asian/Pacific Island or White students than their Hispanic, Mexican counterparts.... In PAGE 22: ... Cuban students, to the contrary, were more likely to be found grouped with Asian/Pacific Island or White students than their Hispanic, Mexican counterparts. Table7 : Over and Under-representation of Hispanic Students Group MEX CUB PR OTHH 1 4.3% -1.... In PAGE 25: ...Page 25 of 28 Table7 : Recommendations for Neural Network Use with Education Policy Analysis Questions Type of Problem Preferred NN(s) Notes Conventional Methods (Comparison/Validation Time Series Prediction (Multivariate) 1. GMDH 2.... ..."

### Table 1: Complexity of the recurrent neural network generated by Algorithm 2.

1995

"... In PAGE 24: ... Boldface transitions have been introduced as escape rules from temporary states. According to Table1 , jN j = m+K+X+3n and jVj = m(m+1)+K(n+1)+4X+K+6n. The numbers K and X depends on the original state transition function.... ..."

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### Table 7 Predictive accuracy obtained by applying neural networks as second level model

"... In PAGE 8: ... The target for this new sample is the same as the class target of the original sample. The accuracy achieved by the second level networks for the diVTerent values of VT and M are given in Table7 . The accuracy on the test set using the best choice criterion achieved by the two-level model is as high as 83.... ..."

### Table 3: The prediction ARV of recurrent networks trained with full input vectors (12-inputs) and

1997

"... In PAGE 10: ...2 Selected Inputs Pi amp; Peterson [10] report very good results using a (6,8,1) network where the input vectors consist of the variables xt?1, xt?2, xt?4, xt?9 and xt?10. Applying the same inputs to our paradigm, indeed improved the prediction results of the ensemble ( Table3 ). The result of 0.... ..."

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### Table 3: The prediction ARV of recurrent networks trained with full input vectors (12-inputs) and

1997

"... In PAGE 10: ...2 Selected Inputs Pi amp; Peterson [10] report very good results using a (6,8,1) network where the input vectors consist of the variables xt?1, xt?2, xt?4, xt?9 and xt?10. Applying the same inputs to our paradigm, indeed improved the prediction results of the ensemble ( Table3 ). The result of 0.... ..."

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