The phrase "approximation is designed to" is correct and usable in written English.
You can use it when discussing the purpose or intent behind a specific approximation in a mathematical, scientific, or technical context.
Example: "The approximation is designed to simplify complex calculations while maintaining a reasonable level of accuracy."
Alternatives: "the estimate aims to" or "the approximation intends to".
A hybrid algorithm integrating the random fuzzy simulation and simultaneous perturbation stochastic approximation is designed to obtain the optimal order quantity.
Second, a nonsingular terminal sliding mode controller (NTSMC) combined with neural network (NN) approximation is designed to track the commanded trajectory.
The general form of the approximation is designed to exploit the Laplace approximations to hypergeometric functions of a single matrix argument presented in Butler and Wood (Ann. Statist. 30 (2002 11555, Laplace approximations to Bessel functions of matrix argument, J. Comput.
Contextual approximations were designed to optimize the automation of approximate reasoning, since the relations between individuals are discovered during the reasoning process.
In particular, fine scale approximations are designed to appropriately represent local behavior by using a localization angle.
Based on the covering linear programming (CLP), a fast iterative approximation scheme is designed to solve this newly formulated problem.
The use of approximation strategies is designed to reduce the number of detailed, costly computer simulations required during optimization while maintaining the pertinent features of the design problem.
First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator.
In addition, a stepped approximation-and-comparison algorithm is designed to compute a more accurate estimate of the range velocity component.
An approximation algorithm with polynomial complexity is designed to solve this model.
Our approach resembles the familiar W.K.B. approximation in one dimension, but is designed to explore the classically forbidden region and has a much wider applicability than W.K.B.
