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The inability to identify the nonlinearities on-line or off-line accurately motivates the design of stabilizing controllers based on approximations or on approximate estimates of the plant nonlinearities.
In a compositional framework, they can be used to approximate interconnections of systems based on approximations of the individual components.
A new suboptimal control method, called the θ-D method, gets an approximate closed-form solution to this nonlinear guidance problem based on approximations to the Hamilton-Jacobi-Bellman equation.
It is based on approximations of generalized Lax pairs.
Existing approaches for the computation of the FIM for those models are all based on approximations of the likelihood.
However, methods based on approximations of limit state surface around the most probable point of failure present a better response in terms of accuracy.
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When β is sufficiently large, an analytical approach, based on approximation at infinity of both exact and approximate nth moments, is used to prove equality between them.
Thus, many variants based on approximation techniques, specially those from Swarm Intelligence (SI), have been proposed.
This paper gives a unified account of state aggregation based on approximation curves.
Then two thresholds, which are based on approximation information and tolerance information, are integrated to evaluate collisions between polygonal models.
Uncertainties in input variables (load, material properties and dimensions structures) are reflected in the RSM method based on approximation of Monte Carlo simulations.
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