### Table 1: Optimal MLP neural network configuration for forecasting wavelet coefficient #9. Layer Nodes Per Layer Nodal Function

"... In PAGE 14: ... Figure 9 illustrates the performance of these MLP networks with different configurations trained using different learning rates. The configuration of the optimal network configuration is depicted in Table1 . This network showed reasonably good generalization with respect to the ninth wavelet coefficient, for both the degradation signals.... ..."

### Table 5.2: In uence of the exibility of the neural network on the approximation of the inverse nonlinear function, for the rst simulation set

### Table 5.10: Approximation of the direct nonlinear function performed by a neural network, for the second simulation set

### Table 1: Neural network estimation results

"... In PAGE 8: ... The decision whether to use the neural net estimation or the analysis tool results can be based on a cost function re ecting the required delity and criticality of the results. Table1 shows four test results of the neural network for the aerodynamic analysis tool. Best results were obtained when the training was done for 1000 cycles with the struc- ture shown in Figure 5 and the learning rate was set to 0.... ..."

### Table I shows the performance of different approaches, and the approach proposed in this paper. It can be seen that wavelet-networks outperform BPN, and this is due to the fact that neural networks based on conventional single-

### Table 2: For test function with two inputs, mean (over 50 data samples) and 95% confidence interval for standardized MSE at 225 test locations, and for the temperature and ozone datasets, cross-validated standardized MSE, for the six methods. Method Function with 2 inputs Temp. data Ozone data

2004

Cited by 4

### Table 2: For test function with two inputs, mean (over 50 data samples) and 95% confidence interval for standardized MSE at 225 test locations, and for the temperature and ozone datasets, cross-validated standardized MSE, for the six methods. Method Function with 2 inputs Temp. data Ozone data

2004

Cited by 4

### Table 2: For test function with two inputs, mean (over 50 data samples) and 95% confidence interval for standardized MSE at 225 test locations, and for the temperature and ozone datasets, cross-validated standardized MSE, for the six methods. Method Function with 2 inputs Temp. data Ozone data

2004

Cited by 4

### Table 3 . Mapping Knowledge Base into Neural Network

"... In PAGE 11: ....2. Correspondences Between Rules and Neural Network In KBANN approach [20, 21], a symbolic explanation-based learner uses a roughly correct domain theory to explain why an example belongs to the target concept. The explanation tree (hierarchical knowledge base) produced is mapped into a neural network : this mapping, specified by Table3 , defines the topology of networks created by KBANN as well as their initial link weights. Table 3 .... ..."

Cited by 1

### Table 1. Objective functions for neural network training

2005

Cited by 1