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In order to optimize the parameters of the nonlinear PID controller using ACO algorithm, an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm.
The final model for all traits based on minimizing the mean squared error was constructed from 1200 trees at an interaction depth of 5 and a shrinkage parameter of 0.0075.
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As such an internal model of first-order fed by the tracking error is constructed.
To take into account the prediction uncertainty, a probability distribution model of the prediction error is constructed using the kernel density estimation method.
The serial parallel estimation model is given and a filter error included tracking error and modeling error is constructed to design the weights updating law of neural networks (NNs).
The system uncertainty is approximated by a composite adaptive LWL neural networks (NNs), in which a prediction error is constructed by using a series-parallel identification model, and NN weights are updated by both the tracking error and the prediction error.
A Poisson model with robust standard errors was constructed and the selection of final covariates was determined through a backwards elimination process to capture joint effects.
Finally the objective function (fleft( {x,y,z} right)) representing assessment errors is constructed.
Then, by utilizing TRMCEs, the mapping relationships between tool path and the numerical control (NC) command without and with considering the geometric errors are constructed respectively.
Furthermore, a more general and unified iterative algorithm called the Mann perturbed iterative algorithm with mixed errors is constructed as well in Hilbert spaces.
Considering that the same features from two neighboring stations coincide after pairwise registration, a dual quaternion-based error norm, which is associated with the sum of the position errors, is constructed.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com