Sentence examples for local linearization from inspiring English sources

For a class of smooth nonlinear multivariable systems whose working-points vary with time and the future working-points knowledge are unknown, a combination of a local linearization and a polytopic uncertain linear parameter-varying (LPV) state-space model is built to approximate the present and the future system's nonlinear behavior, respectively.

This approach of local linearization is further extended to non-linear oscillators excited by formal derivatives of one or a combination of Gauss Markov processes.

On the basis of the RBF-ARX model, a combination of a local linearization model and a polytopic uncertain linear parameter-varying (LPV) model are built to approximate the present and the future system's nonlinear behavior respectively.

Suboptimal Girsanov's controls for estimating the time variant reliability are derived based on component level ideal controls, which are exactly obtainable for linear systems, and, via a local linearization step for nonlinear systems.

For the sake of predicting signal strength measurements at arbitrary locations so as to calculate particle weight and improve localization accuracy, we also adopt a local linearization technique to realize continuous interpolation of the radio map.

The other is the NMPC with local linearization that is applied at each sample instant, a linear model with parameter varying is obtained and the optimization in NMPC can be written as a quadratic programming (QP) problem like linear MPC.

Through local linearizations of the nonlinear drift fields in the process/observation equations via explicit Ito Taylor expansions, the given nonlinear system is transformed into an ensemble of locally linearized systems.

We describe the nonlinear mechanism of the splitting error in such numerical methods in the one-dimensional case, a mechanism that is intimately linked to the local linearizations introduced implicitly in the (hyperbolic) convection steps by the use of an entropy condition.

In this method, non-convex terms are replaced with local linearizations.

The method involves a trajectory based linearization and employs a model bank with a set of local linear models for each discrete operational mode.

In this study, we formulate a certain engineering-based style of interpretation in which a given neural network is represented as a collection of local linear models where such models are developed around a collection of linearization nodes.