Subsequently, we interpret and learn the system using computer vision techniques: First, we present analogies between the system components and computer vision techniques and interpret the network as minimizing a cost functional, thereby establishing a link with variational techniques.
Rather than using a predefined set of forces in the image, variational level set methods seek to produce a level set function that minimises a predefined cost function, more specifically known as energy functional also sometimes called a cost functional.
Considering that the microwave imaging is recast as a nonlinear optimization problem, a cost functional is defined by the norm of a difference between the measured scattered electric field and that calculated for an estimated relative permittivity distribution.
This task is formulated as a constrained optimization problem with a cost functional of least squares type using information obtained from measurements.
The IP algorithm consists on the minimization of a cost functional defined as the quadratic-difference between experimental and trial measurements simulated by the finite element method.
This paper reformulates the two-phase solidification problem (i.e., the Stefan problem) as an inverse problem in which a cost functional is minimized with respect to the position of the interface and subject to PDE constraints.
In a variational approach the data assimilation problem is posed as a minimization problem, which requires the sensitivity (derivatives) of a cost functional with respect to problem parameters.
A quadratic vector performance index is chosen as the cost functional which comprises the functionals of the deflection, velocity and the distributed force.
The optimal actuator location problem is subsequently formulated as the one of minimizing a meaningful cost functional that includes penalty on the response of the closed-loop system and the control action.
The optimal actuator location problem is subsequently formulated as the one of minimizing a meaningful cost functional that includes penalty on the response of the closed-loop system and the control action and is solved by using standard unconstrained optimization techniques.
