### Table 1: Standard basis functions used in radial basis function networks.

1994

"... In PAGE 16: ... 3.2 Networks with xed centers Table1 summarizes some of the usual basis functions used in RBFNs. The use of these functions is justi ed by the theory introduced above [32].... ..."

Cited by 3

### Table 1. Radial basis functions and Fourier transforms

1999

"... In PAGE 2: ...2) is symmetric and positive de nite on (Pd m)?. Table1 shows some conditionally positive de nite functions with their minimal orders m. Any functional 2 (Pd m)? of the form (2.... In PAGE 9: ...5) that makes the integral well{de ned near zero. Table1 shows the functions ^ for various choices of . As a referee correctly pointed out, the assumption (4.... ..."

Cited by 24

### Table 1: Radial basis functions and Fourier transforms

"... In PAGE 4: ...2) is symmetric and positive de nite on (IP d m)?. Table1 shows some conditionally positive de nite functions with their minimal orders m. Any functional 2 (IP d m)? of the form (2.... In PAGE 11: ...5) that makes the integral well{de ned near zero. Table1 shows the functions ^ for various choices of . As a referee correctly pointed out, the assumption (4.... ..."

### TABLE 5. Percentage of test data patterns correctly identified or rejected as unknown by each of two criteria for 20 known species (on which the network had been trained) and for 14 novel speciesa

1999

Cited by 1

### Table 2. Comparison of the number of radial basis functions.

"... In PAGE 4: ...It should be noted that the number of RBFs created by each algorithm depends on the values of the parameters. In Table2 , the numbers of RBF terms reported are for classi- fiers that give the smallest estimated generalization errors. As we can see, on most of the datasets, the LP SC algo- rithm creates fewer spheres than the RCE network.... ..."

### TABLE 3. Effect of exclusion of each parameter on the percent correct identification of an RBF ANN trained to discriminate 34 speciesa

1999

Cited by 1

### Table 1. Radial basis functions

1999

Cited by 17

### TABLE I RADIAL BASIS FUNCTIONS

### Table 5. Results of monthly evaporation forecasts with radial basis functions

"... In PAGE 6: ... The input layer of the neural networks constitutes of previous monthly evaporation losses, monthly mean ( x ) and standard deviation (sx) and the periodic component (PI). The results for the testing period are tabulated in Table5 and Table 6 and plotted in Figure.5 and Figure 6 for both neural network methods.... In PAGE 6: ...ethods. The best estimation result is obtained for an input configuration with 5 inputs for RBF. Here the input layer includes two past monthly evaporation values in addition to monthly mean, standard deviation, and periodic component values (MSE=7x10-5 hm6; R2=0.9994; Table5 ). For FFBP on the other hand, an input layer with 7 inputs provides lowest MSE (MSE=14x10-4 hm6; R2=0.... ..."