System identification

About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.

Monitoring Impact Events Using a System-Identification Method

Abstract: An inverse method based on a system-identification technique for identifying impact events on a complex structure with built-in sensors is presented. The method using the transfer functions in the system-identification technique identifies the location and force time history of an impact event on a structure without the need of constructing a full- scale accurate structural model or of acquiring excessive training data on the structure, such as neural-network techniques. The system transfer functions for the entire structure are constructed by two sequential procedures: 1) limited impact tests at selected points to establish the system transfer functions from the selected points to a sensor on the structure and 2) an interpolation function approach based on a linear finite element to approximate the system transfer functions from a point inside four neighboring selected points to the sensor. Comprehensive tests with various impact situations verified the accuracy of load and position predictions by the proposed method.

Adaptive H^∞ filter: Its application to structural identification

Abstract: By adding the function of memory fading for past observation data to the \iH\d∞ filter, the adaptive \iH\d∞ filter was developed for identifying structural systems with nonstationary dynamic characteristics. Identification algorithms are proposed for time-varying structural systems in which the velocity and displacement of each floor are available for observation, as well as for the case when only the velocity and displacement of some floors are available. The Akaike-Bayes information criterion is used to determine the optimal forgetting factor. Identification algorithms that use the adaptive \iH\d∞ and Kalman filters are applied to a five-degree of freedom (DOF) linear system with nonstationary dynamic characteristics and to a five-DOF nonlinear structural system. Digital simulation results show that the adaptive \iH\d∞ filter efficiently traces the time-varying properties of structural systems. The behavior of the adaptive \iH\d∞ filter is better than that of the adaptive Kalman filter for identifying a structural system with time-varying dynamic characteristics. The former is more efficient and robust for identifying structural systems with nonstationary dynamic characteristics.

Numerical Data Fitting in Dynamical Systems

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