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For most of the applications, a single hidden RBF neuron itself will be sufficient and for other complex problems the procedure starts with a single hidden neuron and sequentially increases the number of hidden neurons until the model error becomes sufficiently small.
The mean model error becomes very low at a β factor of 1.
At a factor value of 4, the mean model error does not decrease monotonically with respect to the number of incorporated enzymes and the mean model error becomes larger than any other value of β.
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The allowable ranges of the model error became narrow significantly (data not shown).
The model converging error becomes saturated and slightly changes after the around thousand epochs.
At higher velocities the error becomes unacceptably large.
However, regardless how small the average error becomes with any individual system, large errors within any particularly piece of guidance are still possible on any given model run.
However, regardless how small the average error becomes with any individual system, large errors within any particular piece of guidance are still possible on any given model run.
If the error decreases, the increasing step is continued until the error becomes constant or drops.
The BEM modeling error will become larger if more OFDM symbols are considered and/or the channel varies faster.
Margins for error become tight.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com