### Table 1. Test results and performance comparison of forex forecasting

2003

"... In PAGE 9: ...029 0.020 Test results Table1 summarizes the training and test performances of the neuro-fuzzy system and neural network. Figure 4 shows the developed Takagi-Sugeno type fuzzy inference model for forex prediction.... ..."

Cited by 1

### Table 2. Performance of the di erent paradigms.

"... In PAGE 9: ... Figures 9 and 10 illustrate the meta-learning approach combining evolutionary learning and gradient descent technique during the 35 generations. Table2 summarizes the performance of the devel- oped i-Miner for training and test data. Performance is compared with the previous results reported by Wang et al.... In PAGE 9: ... (2002) wherein the trends were analyzed using a Takagi-Sugeno Fuzzy Inference System (ANFIS), Arti - cial Neural Network (ANN) and Linear Genetic Program- ming (LGP). The Correlation Coe cient (CC) for the test data set is also given in Table2 . The 35 generations of meta-learning approach created 62 if-then Takagi{Sugeno type fuzzy rules (daily tra c trends) and 64 rules (hourly tra c trends) compared to the 81 rules reported by Wang et al.... In PAGE 13: ... The knowledge discovered from the developed FCM clusters and SOM could be a good comparison study and is left as a future research topic. As illustrated in Table2 , i-Miner framework gave the overall best results with the lowest RMSE on test error and the highest correlation coe cient. It is interesting to note that the three considered soft computing paradigms could easily pickup the daily and hourly Web-access trend patterns.... ..."

### 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 systems of nonlinear plant.

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

### Table 1. Comparison of code sizes for the FIR-filter designs

1998

"... In PAGE 6: ...y an enable signal from the sum unit. The third model is the fully synchronous model. It runs at half the clock speed as the other two, since it does not have any Clock Protocol that need to derive an internal clock from a faster, external, one like the other two protocols need. The number of ProGram lines of code and the number of VHDL lines of code needed to describe in the FIR-filter designs we are using our case study, are shown in Table1 . As can be seen, the ratio is around 6 times less, which means that the final design should have 6 times less bugs if the number of bugs per lines are the same for both languages.... ..."

Cited by 2

### Table 1. CSD representation of FIR

1999

"... In PAGE 2: ... A two-term can appear in more than one constant. If we examine Table1 , it can be easily observed that the two-term 17 appears in the CSD representations of constants a, c and d.Wecan express the constants in the system using two-terms.... In PAGE 2: ... After the decision on two-terms and replicas for additions, the second aim is minimization of shifting operations. For the FIR example of Table1 , only three two-terms are sufficient as it can be seen in Fig. 1.... ..."

Cited by 2

### Table 1. Impulsive noise models; envelope PDFs and LO nonlinear filters.

2002

"... In PAGE 13: ...onparametric filters. These require no explicit knowledge of the noise PDF. An example is the hardlimiter narrowband correlator (HNC) filter ([8, 10]) which is widely used in impulsive environments; y y g 1 ) ( = (4) The parametric version of the processor requires a choice of noise model, and estimation of the model parameters from the received data. The LO filters for several impulsive noise models are given in Table1 . To apply the processor, we read-in a segment of time-series data, estimate the model parameters from that segment of data,9 and then input these parameter estimates into the nonlinear filter to tune the processor.... ..."

### Table 1. Shifts in matrix models outlining the evo- lution of recommender systems from information retrieval.

"... In PAGE 5: ... While IR entails returning relevant in- formation in response to short-term information-seeking goals via requests such as queries, information filtering involves removing persistent and irrelevant information over a long period of time. Information filtering systems model document features in user profiles [71], which replaced terms in a modeling matrix as a result of this shift (see Table1 ). Infor- mation filtering later became known as content-based filtering to the recommender system community and has been applied to recommend movies [4] and books [71].... In PAGE 6: ... This final shift replaced documents with artifacts in the modeling matrix. While the evolution of recommender systems research is characterized by the shifts in matrix models illustrated in Table1 , the sparsity and anti-symmetric properties remained constant across each. As shown below, the web makes the matrix model symmetric.... ..."

Cited by 1