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After that ANN model was optimized by unified particle swarm optimization (UPSO).
The model was optimized for biomass production with the optimal flux of 0.48 doublings per hour when grown on cellobiose with a substrate uptake rate of 0.25 mmole/h.
For prediction, the model was optimized using an iterative procedure rather than simultaneous optimization of all parameters using Baum-Welch.
According to the verification the computation model was optimized.
The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing.
The model was optimized to determine the feed and shell flow rates which maximize the yield.
Then, the model was optimized using the proposed evolutionary structure based upon GA.
The model was optimized for the maximum biosurfactant production by using genetic algorithm.
The developed model was optimized to maximize the tensile strength using generalized reduced gradient method.
Initially, the geometric configuration of the enhanced model was optimized by using response surface methodology.
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The pediatric PBPK model was optimized following development of an adult PBPK model that adequately described literature data.
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CEO of Professional Science Editing for Scientists @ prosciediting.com