Sentence examples for system parameter identification from inspiring English sources

The lack of data pairs is addressed by transforming the model-training problem into a dynamic system parameter identification problem.

In this work, two of these approaches, smooth orthogonal decomposition (SOD) and direct system parameter identification (DSPI), are analyzed, compared, and improved upon.

It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus allows training the empirical model part with only measured data.

MWEKF is more robust and adaptive in structural damage detection compared with classical EKF because of the following reasons: (1) it is insensitive to the selection of the initial state vector; (2) it exhibits more accurate system parameter identification; and (3) it is immune to the inaccurate assumption of noise levels because measurement and process noise levels are estimated in this approach.

The special issue also contains results on the exact controllability of impulsive differential equations, synchronization of complex networks, converse Lyapunov theorems, robust stabilization of fractional systems, parameter identification and synchronization of dynamical systems, and some special topics of automorphisms of submanifolds.

In this report, we present an extension of our previous optimization algorithm for S-system parameter identification from time series data [ 14].

The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression.

In this report, we propose a new method, inspired by AR and based on multiple linear regression and sequential quadratic programming (SQP) optimization, to address the S-system parameter identification problem when no information about the network topology is known.

This includes hardware design, sensor and actuator selection, system modeling, parameter identification, controller design and experimental testing.

(3) Figure 2 A laser tracker measurement system for parameter identification of industrial manipulators.

Third, instrumentation plays different roles in the estimation of the system parameters, delivering identification for the structural coefficients in the usual way, but raising convergence rates for the threshold effect parameters and improving efficiency for the threshold point.