Difference between revisions of "Bilateral Trigeminal neuromotor organic symmetry"

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<math>CC-Onset-Latency=\tfrac{Right-Onset-Latency}{Left-Onset-Latency}</math>                                                                                     <math>Eq.4.1</math></blockquote><blockquote><math>CC-Amplitude=\tfrac{Right-Amplitude}{Left-Amplitude}</math>                                                                                                        <math>Eq.4.2</math></blockquote><blockquote><math>CC-Integrated-Area=\tfrac{Right-Integrated -Area}{Left-Integrated -Area}</math>                                                                                <math>Eq.4.3</math></blockquote>
<math>CC-Onset-Latency=\tfrac{Right-Onset-Latency}{Left-Onset-Latency}</math>      


<math>Eq.4.1</math></blockquote><blockquote><math>CC-Amplitude=\tfrac{Right-Amplitude}{Left-Amplitude}</math>         
<math>Eq.4.2</math></blockquote><blockquote><math>CC-Integrated-Area=\tfrac{Right-Integrated -Area}{Left-Integrated -Area}</math>                 
<math>Eq.4.3</math></blockquote>


Therefore we first defined the characteristics of the network, defined the appropriate input and desired output (called target into the ANN) of the network. Then we adopted the LM algorithm to train the network as described above. But we used the ANN to test the correlation between the EMG values of the right and left muscles.
Therefore we first defined the characteristics of the network, defined the appropriate input and desired output (called target into the ANN) of the network. Then we adopted the LM algorithm to train the network as described above. But we used the ANN to test the correlation between the EMG values of the right and left muscles.
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