Sentence examples for constrained estimation from inspiring English sources

Section 4.1 formulates the black-border constraints as linear inequalities for most of the 2D camera motion models, and shows how estimate projection can be used to solve the constrained estimation problem.

Consider the following constrained estimation problem: (25).

Finally, illustrative examples of chemical process systems and proposed optimal state constrained estimation are presented.

A variant of the CRB for this kind of the constrained estimation problem was developed in [20, 22], which is called the constrained CRB.

However, the use of ICA for operational crop monitoring will require algorithms that will take into account prior information on crop growth curves, constrained estimation of independent components, and scaling of mixing vectors to obtain physically possible ranges.

Design of a direct MRAC, whose controller gains are continuously adjusted to accommodate changes in process dynamics and disturbances, is investigated, including simulation-based robustness analysis of the adaptation law and a consideration of constrained estimation in the recursive least squares algorithm to address practical implementation issues.

In other words, the estimation of the average import/export flows should be checked to be double constrained estimations since the imports of a zone (presented as origin from a specific destination) must be equal to the export of the specific destination to the presented origin.

Their bounds were set to span an arbitrarily large range of values without constraining estimation.

Since government plays a key role in the estate development in China, the housing floorspace is estimated through three steps including unconstrained floorspace estimation, constrained floorspace estimation, and zonal floorspace allocation.

In this paper, we consider the inequality constrained parameter estimation problem.

At the root of the success of kernel-based learning, the combination of high expressive power with the possibility to perform the numerous analyses has been developed in many challenging applications [65], e.g., online classification [66], convexly constrained parameter/function estimation [67], beamforming problems [68], and adaptive multiregression [69].