### Table 2. Neural network modeling

2006

"... In PAGE 5: ... In model A, we calculated the IBIs from the steady-state solution, whereas in model B we continuously solved the network without discarding the transients. The model param- eters are summarized in Table2 . The parameters were the same as previously described (4), except for TNI1 (see Table 2).... ..."

### Table 1. Neural Network Models.

### Table 1. Comparison of the HCMAC neural network with the MHCMAC neural network Models

"... In PAGE 15: ... D. Comparison of HCMAC Neural Network with the MHCMAC Neural Network Table1 compares the HCMAC neural network with the MHCMAC neural network in terms of memory requirement, topology structure and input feature assignment approach. Table 1 shows that the memory requirement of the original HCMAC neural network grows with the power 2 of the ceiling logarithm of the input dimensions, but the memory requirement of the MHCMAC neural network grows only linearly with the input feature dimensions.... In PAGE 15: ... Comparison of HCMAC Neural Network with the MHCMAC Neural Network Table 1 compares the HCMAC neural network with the MHCMAC neural network in terms of memory requirement, topology structure and input feature assignment approach. Table1 shows that the memory requirement of the original HCMAC neural network grows with the power 2 of the ceiling logarithm of the input dimensions, but the memory requirement of the MHCMAC neural network grows only linearly with the input feature dimensions. Moreover, the learning structure of the self-organizing HCMAC neural network is expanded based on a full binary tree topology, but the MHCMAC neural network is expanded based on an exact binary tree topology.... ..."

### Table 2 Model Mass Inertias

1999

"... In PAGE 3: ... The rst entry in the table is the case designator. Table2 presents the associated moments of inertia. The models were constructed of high density foam and berglass in two sections.... ..."

Cited by 2

### Table 2 Model Mass Inertias

1999

"... In PAGE 3: ... The #0Crst entry in the table is the case designator. Table2 presents the associated moments of inertia. The models were constructed of high density foam and #0Cberglass in two sections.... ..."

Cited by 2

### Table 4 Mean and standard deviation of errors in estimated mass of fish using three mass models and using truss lengths derived in three waysa

2000

"... In PAGE 15: ...5. Mass estimations The truss length data generated in the various ways were introduced into three of the morphological models to obtain estimates of mass ( Table4 ). The three models chosen were all suitable for the weight range of fish examined.... ..."

### Table 1: Summary of low-mass Standard Model Higgs search channel sensitivities used in the combined integrated luminosity threshold calculations. The values of S and B are expressed as the number of events expected in 1 fb?1, and S=pB is a pure number. Here we assume an improved Run 2 mb b resolution of 10%. \SHW quot; indicates the analyses based on teh SHW simulation, \NN quot; indicates the SHW neural-network-based analyses, and \CDF quot; indicates the analyses based on extrapolations from the CDF Run 1 conditions to Run 2 detector geomentry and e ciencies.

"... In PAGE 5: ... In the real experiment, however, one will employ a full mass spectrum t to signal and background. As Table1 shows, the CDF and SHW analyses have quite similar results,... In PAGE 6: ... The main discriminating power comes from the b b mass resolution, and the kinematics of the heavy avor production; the b b pairs in QCD processes tend to be at lower pT than in W H and ZH events. As Table1 shows, for an analysis based on SHW and backgrounds normalized to Run 1 D data, the sensitivity is... ..."

### Table 1: Summary of low-mass Standard Model Higgs search channel sensitivities used in the combined integrated luminosity threshold calculations. The values of S and B are expressed as the number of events expected in 1 fb?1, and S=pB is a pure number. Here we assume an improved Run 2 mb b resolution of 10%. \SHW quot; indicates the analyses based on teh SHW simulation, \NN quot; indicates the SHW neural-network-based analyses, and \CDF quot; indicates the analyses based on extrapolations from the CDF Run 1 conditions to Run 2 detector geomentry and e ciencies.

"... In PAGE 7: ... In the real experiment, however, one will employ a full mass spectrum t to signal and background. As Table1 shows, the CDF and SHW analyses have quite similar results,... In PAGE 8: ... The main discriminating power comes from the b b mass resolution, and the kinematics of the heavy avor production; the b b pairs in QCD processes tend to be at lower pT than in W H and ZH events. As Table1 shows, for an analysis based on SHW and backgrounds normalized to Run 1 D data, the sensitivity is... ..."

### Table 1: Total number of Neural Network Models

"... In PAGE 3: ... Table1 : Total number of Neural Network Models One method to speed the modeling process was to increase node additions by two. To illustrate these concepts, consider a neural network with 3 inputs and 1 output.... In PAGE 3: ...-1-1-1; ... ; 3-3-5-5-1; and 3-5-5-5-1. For N inputs, the number of neural network architecture permutations equals: 1 + N + N2 + N3 where 1 means there is only one neural network architecture with zero hidden layers, N is the number of ways of creating a neural network architecture with one layer, N2 is the number of ways of creating a neural network architecture with two layers, and N3is the number of ways of creating a neural network architecture with three layers. Table1 shows the total number of permutations of neural network architectures, metric categories, and group configurations. In order to build and train 33,190 neural networks, an automated neural network program was used.... ..."

### Table 4: Neural Network Modeling of Bandpass Filter

"... In PAGE 4: ...oss was 1-2.5 dB. Sample measured results are shown in Figure 9. The measured data was used to obtain a neural network model ( Table4 ) for the center frequency, bandwidth, and minimum pass band insertion loss. Figure 8.... In PAGE 4: ...3 mm-Wave Filter Synthesis Results The neural network models were used to design LTCC mm-wave low pass and band pass filters using the neuro- genetic approach. The genetic algorithm parameters chosen for filter synthesis are shown in Table4 . These parameters were chosen such that the algorithm converged to the desired optimal point with few iterations.... ..."