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This paper presents the NN methodology used in developing the models, the accuracy of the predictions, and some sample results.
However, as increasingly complex processes are included in these models, the accuracy of the numerical formulation coupling the nonlinear processes becomes difficult to verify.
The mechanical properties of such steel anti-collision devices subjected to vessel impact loadings often need to be investigated by means of finite-element simulations using numerical models, the accuracy of which is critical.
For solving above kinetic models, the accuracy of the estimated result is dependent on the calculated method.
Hence, the probability that a predicted parameter at slot t ≥ t c is not equal to the actual parameter can be described as 1 − ( 1 − e ) t − t c, where e∈[0…1] models the accuracy of the prediction.
Since the use-exposure relationships were parameterized from regression analysis on the simulation results of existing field-scale models, the accuracy of risk assessment based on those relationships is associated with the model itself and the configuration of crop scenarios.
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Over time, as more of these factors were identified and fed into the model, the accuracy of the forecast improved.
Considering relative simplicity of the model, the accuracy of the PAFRAG code predictions is rather remarkable.
Then for a given noise and image model, the accuracy of the image tracking was evaluated.
Including phoneme probabilities into the recognition process (unigram language model), the accuracy of the recognition improves significantly.
When the probability confidence map is used as the prediction model, the accuracy of SVM classifier cannot achieve 100%.
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