The proposed approach gathers recent advances in aeroelastic modelling and model approximation techniques.
Ant state prediction aims to guide ant clustering around cells of interest, while pheromone field prediction helps to accelerate the pheromone formation of the current frame using the Gaussian mixture model (GMM) approximation technique.
Although these models include approximation techniques, they only evaluate one goods delivery scenario at a time without considering scenario selection.
Therefore, the rigorous model containing (partial) integro-differential equations is first reduced by applying an integral approximation technique to a model of moments that consists of only a few ordinary differential equations.
Models were developed using genetic function approximation technique and were extensively validated employing different validation statistics.
A best matrix approximation technique for updating the analytical model is developed using the known modal parameters.
Then, a polytopic approximation technique is applied to obtain a discrete-time model of the closed-loop CAN system.
The function approximation technique using neural networks is employed to estimate the model uncertainties of each follower.
This control strategy employs the functional approximation technique to establish the unknown function for releasing the model based requirements of the sliding-mode control.
Base techniques used in this work comprise geometric primitive model approximation (e.g., RANSAC), Region Growing segmentation, and a graph theory-based approach (particularly the "Min-Cut" algorithm).
In this section, the smooth approximation technique and FOCUSS algorithm are introduced to the sparse sampling model of RBSR and realized sparse signal reconstruction.
