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Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications
by Lakhmi C. Jain; N.M. Martin CRC Press, CRC Press LLC ISBN: 0849398045 Pub Date: 11/01/98 |
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Results reported in Table 2 confirm the hypothesis that configuration Net0 independently minimizes the cost figure for each parameter outperforming NetA and NetB In the case of Net0, in fact, the weights of each TDNN are tuned independently toward a unique objective in contrast to NetA and NetB where 5 uncorrelated objectives must be met by the same, although larger structure.
T1 | PARAMETER | ||||||||
---|---|---|---|---|---|---|---|---|---|
LM | H | W | |||||||
Cost | net0 | netA | netB | net0 | netA | netB | net0 | netA | netB |
MSE | 2.761 | 3.665 | 3.358 | 3.380 | 4.691 | 4.150 | 3.762 | 3.460 | 2.312 |
MAX | 0.863 | 1.072 | 0.762 | 0.783 | 1.103 | 0.773 | 0.653 | 0.732 | 0.771 |
r | 0.9435 | 0.9186 | 0.9269 | 0.9386 | 0.9060 | 0.9198 | 0.8586 | 0.7855 | 0.7777 |
T1 | PARAMETER | ||||||||
Lup | DW | Average | |||||||
Cost | net0 | netA | netB | net0 | netA | netB | net0 | netA | netB |
MSE | 1.994 | 3.717 | 3.687 | 5.531 | 9.933 | 8.969 | 3.015 | 4.646 | 4.495 |
MAX | 0.704 | 0.911 | 0.871 | 1.020 | 1.078 | 1.148 | 0.804 | 0.972 | 0.865 |
r | 0.8090 | 0.5966 | 0.6031 | 0.7067 | 0.4448 | 0.4387 | 0.8513 | 0.7303 | 0.7330 |
T2 | PARAMETER | ||||||||
---|---|---|---|---|---|---|---|---|---|
LM | H | W | |||||||
Cost | net0 | netA | netB | net0 | netA | netB | net0 | netA | netB |
MSE | 5.782 | 6.622 | 6.148 | 7.091 | 8.008 | 7.309 | 3.762 | 3.460 | 3.700 |
MAX | 0.988 | 1.006 | 0.957 | 1.095 | 1.066 | 1.106 | 1.132 | 1.071 | 1.097 |
r | 0.8599 | 0.8342 | 0.8489 | 0.8474 | 0.8260 | 0.8420 | 0.5619 | 0.5887 | 0.5655 |
T2 | PARAMETER | ||||||||
Lup | DW | Average | |||||||
Cost | net0 | netA | netB | net0 | netA | netB | net0 | netA | netB |
MSE | 16.18 | 18.27 | 18.75 | 10.93 | 10.12 | 10.03 | 8.569 | 9.296 | 9.187 |
MAX | 1.384 | 1.430 | 1.490 | 1.354 | 1.090 | 1.210 | 1.190 | 1.132 | 1.172 |
r | 0.3645 | 0.2937 | 0.2858 | 0.2701 | 0.2928 | 0.3184 | 0.5807 | 0.5671 | 0.5721 |
NetB yields average performances higher than NetA confirming that the optimal size for the time-window on which the articulatory estimates are based equals 260 msec. while 380 msec time-windows (19 × 20 msec) are used in the case of NetA.
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