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Difference between revisions of "Bilateral Trigeminal neuromotor organic symmetry"
Bilateral Trigeminal neuromotor organic symmetry (view source)
Revision as of 15:56, 8 March 2022
, 2 years ago→Artificial neural network model
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An artificial neural network (ANN) is a general mathematical computing paradigm by which the geometry and functionality of the ANN have been linked to the biological neural system and one of the most interesting characteristic of this paradigm is the self-learning propriety. | An artificial neural network (ANN) is a general mathematical computing paradigm by which the geometry and functionality of the ANN have been linked to the biological neural system and one of the most interesting characteristic of this paradigm is the self-learning propriety. | ||
The ANN computational model has been used to estimate the correlation coefficient with the EMG values of one side. With the ANNs, we can determine the correlations that describe input/output formulation in a dataset or a system. <ref name=":5" /><ref name=":6" /> | The ANN computational model has been used to estimate the correlation coefficient with the EMG values of one side. With the ANNs, we can determine the correlations that describe input/output formulation in a dataset or a system.<ref name=":5" /><ref name=":6" /> | ||
If organic symmetry exists, there would be a correlation coefficient between the EMG values of the right and left muscles. To test this assumption, we adopted the ANN model. First we created, configured, and initialized our multi-layer ANN.<ref name=":6" /> | If organic symmetry exists, there would be a correlation coefficient between the EMG values of the right and left muscles. To test this assumption, we adopted the ANN model. First we created, configured, and initialized our multi-layer ANN.<ref name=":6" /> | ||