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At low input signal intensity, S→0, the output is strongly (linearly) sensitive to changes in input, with the output changing in proportion to S. At high signal intensity, the output is weakly (logarithmically) sensitive to changes in input, with the output changing in proportion to log(S).
On the other hand, little is known about the ultimate limitations of control when the models in question are not linear, in which case small changes in input can result in large deviations.
A detailed evaluation of the effects of changes in input parameters was also carried out.
Further, the sensitivity analyses showed that these results were robust to changes in input parameters.
To answer the question, we follow up with changes in input data.
It is batch-oriented so changes in input require full execution from scratch.
This model can reproduce the complex temperature response of DOC output to changes in input variables.
There was moderate redundancy between sub-indices but considerable sensitivity to changes in input variables.
The proposed method uses the LMS algorithm to adaptively compensate changes in input amplitude and frequency.
Since luscus can detect changes in input files, the result will be automatically displayed on the screen.
Homeostatic scaling adjusts the strength of synaptic connections up or down in response to large changes in input.
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CEO of Professional Science Editing for Scientists @ prosciediting.com