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Results and geometric inputs were then used to develop an artificial neural network model that predicts the flexural rigidity of a bridge based on the measured modal frequencies derived from vibration testing.
These newly generated inputs were then evaluated and the same process was repeated for one thousand runs so that these data best-matched with the other data, or best-represented the current knowledge state, and are denoted by {(X i and data)1, (X i and data)2,…, (X i and data) q }.
Dose-response characterization data and inputs were then submitted by all teams and subsequently reviewed and compiled by N.K., A.Co. and R.M.
The immunoprecipitated samples, along with inputs, were then subjected to Western blot analysis using affinity-purified polyclonal anti- caspase 2) or monoclonanti- caspase antibodies.
Those inputs were then optimized, in the gas phase, by means of a molecular mechanics method (MM+ as implemented in HyperChem software) and a DFT method (RB3LYP/LANL2DZ for Na+ species and RB3LYP/6-31+G* RB3LYP/6-31+G*es).
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Such feedback control inputs are then compared with traditional bang-bang type strategies and turn out to be more robust.
To achieve good precision, inputs are then shaped for the closed loop system to track a raster pattern.
All of these inputs are then combined with a detailed map stored on an onboard computer, to keep track of the car's exact position.
The median performance for all single inputs is then analysed and the inputs ranked accordingly.
These fuzzy inputs are then used in the inference engine to generate fuzzy outputs.
A modified model with more flexible inputs was then developed to address these limitations.
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CEO of Professional Science Editing for Scientists @ prosciediting.com