### Table I N = number of zeros, = fractional order, T location of the largest zero.

### Table 1: A List of Laplace and Inverse Laplace Transforms Related to Fractional Order Calculus

2001

### Table 2: A List of Laplace and Inverse Laplace Transforms Related to Fractional Order Calculus (Continued)

2001

### Table 2. Controller parameters. Fractional order Joint 3 mechanically actuated Joint 3 motor actuated

2006

"... In PAGE 10: ... Afterwards, we repeat the controller tuning procedure for the case where all three joints are motor actuated. The controller parameters, for both cases, are presented in Table2 and the corresponding... ..."

### Table 3: A List of Laplace and Inverse Laplace Transforms Related to Fractional Order Calculus (Mittag-Le er function type)

2001

### Table 5. Comparative classification percentages of the neural network with respect to the opti- mized neural network.

2007

"... In PAGE 9: ... Comparative classification percentages of the associative memory using median opera- tor with respect to the optimized associative memories M 32 M 64 M 96 M Bolt 100% 100% 100% 100% Washer 100% 100% 100% 100% Eyebolt 90% 90% 90% 90% Hook 50% 50% 50% 50% Dovetail 85% 85% 85% 85% For the case of the multilayer neural network trained with the back-propagation al- gorithm, the performance for , and was 89 %, 100% and 100%, while for M it was 98%. Table5 summarizes the classification results for all neural networks trained with the back-propagation algorithm. 32 M 64 M 94 M Table 5.... ..."

### Table 3: Pair-based performance of Bidirectional Recursive Neural Networks trained with multiple alignment profiles.

2004

"... In PAGE 7: ... One additional index we use is Q2, which estimates the probability of correct prediction at the level of individual pairs of bonded cysteines, either in contact or not. Table3 shows the results of this kind of analysis. We report performance grouped according to the topology class and the number of disulfide bonds of each chain.... ..."

Cited by 26

### Table 3: Pair-based performance of Bidirectional Recursive Neural Networks trained with multiple alignment profiles.

2004

"... In PAGE 7: ... One additional index we use is Q2, which estimates the probability of correct prediction at the level of individual pairs of bonded cysteines, either in contact or not. Table3 shows the results of this kind of analysis. We report performance grouped according to the topology class and the number of disulfide bonds of each chain.... ..."

Cited by 26

### TABLE II COMPARISON OF FEEDFORWARD (FFNN) [11], BIDIRECTIONAL RECURRENT (BRNN) [21] AND BIDIRECTONAL LONG SHORT-TERM MEMORY (BLSTM) NEURAL NETWORK PREDICTION PERFORMANCES ON NOVEL NON-PLANT TEST-SETS IN FIVEFOLD CROSS-VALIDATION.

### 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.... ..."