### 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: Average network size for Fuzzy ARTMAP and network size of Ordered Fuzzy ARTMAP with nclust = (number of classes) + 1

1999

"... In PAGE 12: ... Negative percentages imply that the corresponding Fuzzy ARTMAP generalization performance (worst, average, or best) is better than the Ordered Fuzzy ARTMAP generalization performance. In Table2 , we show the size of the network that Ordered Fuzzy ARTMAP created and the average size of the network that Fuzzy ARTMAP created. It is worth pointing out that the size of the neural network architectures that Ordered Fuzzy ARTMAP creates range between 0.... In PAGE 12: ... This analysis shows that in both cases the number of operations required is O(PT), where the constant of proportionality in the Ordering Algorithm is approximately equal to n2 clust, while the constant of proportionality in Fuzzy ARTMAP is approximately equal to PE e=1 ne, where ne is the average number of categories in Fa 2 during the e-th epoch of training, and E is the average number of epochs needed by Fuzzy ARTMAP to learn the required task. As can be seen in Table2 , there are... ..."

Cited by 6

### Table 1: Architectural specifications of the hybrid neural network architecture

"... In PAGE 3: ... These Hebbian connections are used to spread the activations from one Kohonen map to another such that a localised activity pattern in either Kohonen map will cause a corresponding localised activity pattern on the other Kohonen map, and this would be the basis of concept lexicalisation. Table1 gives the architectural specifcations of the three neural networks to be used for the simulation with detailed description to follow in the forthcoming discussion. Table 1: Architectural specifications of the hybrid neural network architecture ... ..."

### Table 2. Profits for the 1995 market simulation of the BHS, the LAM, and two Neural Network Approaches.

1997

"... In PAGE 5: ... the same simple trading strategy, previously described, and using the predictions of our neural networks. The profitabil- ity of the market simulation for 1995 for the BHS, the LAM, and the trading based on predictions of two NNS is sum- marized in Table2 . Our two selected neural networks for the DM predictions initiated 29 and 36 round-trip transac- tions, which is much less comparedwith 74 round-triptrans- actions for the LAM.... ..."

Cited by 1

### Table 1: Criteria for choosing neuro{fuzzy combinations

1994

"... In PAGE 4: ...b, because they create the rules completely out of training data. In Table1 the NEFCON model is compared to hybrid approaches that are only able to learn membership functions. The NEFCON architecture can be interpreted as a neural network and as a fuzzy controller.... In PAGE 4: ... For this reason it is suited for all kinds situations, where any kind of neuro{fuzzy model is applicable. Table1 displays under which circumstances the discussed generic models can be applied. If the decision for... ..."

Cited by 4

### Table 5: Fuzzy and neuro-fuzzy software systems.

2003

"... In PAGE 22: ...upports independent rules (i.e., changes in one rule do not effect the result of other rules). FSs and NNs differ mainly on the way they map inputs to outputs, the way they store information or make inference steps. Table5 lists the most popular software and hardware tools based on FSs as well as on merged FSs and NNs methodologies. Neuro-Fuzzy Systems (NFS) form a special category of systems that emerged from the integration of Fuzzy Systems and Neural Networks [65].... ..."

Cited by 2

### TABLE 8 SIMULATED ANNEALING ON THE NEURAL NET

1994

Cited by 42

### Table 2: Performance of fuzzy ARTMAP, Cascade ARTMAP, and Cas- cade ARTMAP rules on the promoter data set comparing with the sym- bolic learning algorithm ID-3, the KNN system, consensus sequence anal- ysis, the backpropagation network, the KBANN system, and the NofM rules.

"... In PAGE 26: ...voting was also used in the KBANN system [20]. Table2 compares the performance of fuzzy ARTMAP and Cas- cade ARTMAP, averaged over 20 simulations, with other alternative systems. Among the systems that do not incorporate a priori sym- bolic knowledge, fuzzy ARTMAP (Cascade ARTMAP without rule insertion) achieves the lowest error rate.... ..."

### Table3-1. Analog VLSI vs.Digital VLSI ComputingPerformanceComparison

1996