Dynamic identification models allow for system parameters such as mass and inertia to be computed by combining knowledge of the system’s configuration, and the forces applied to the system. These methods are often used to calibrate individual dynamic models in robots, and have also been used to estimate human parameters. While commonly used, the Denavit-Hartenberg approach can lead to modelling...

Metabolomics studies have gained major importance in food applications. Quality control of food samples can be established on a large scale via state-of-the-art metabolic profiling based on statistical data evaluation. However, the metabolomics workflow puts high demands on an analytical system in terms of its robustness, dynamic range and identification capabilities. The Bruker compact QTOF Sy...

an adaptive version of growing and pruning rbf neural network has been used to predict the system output and implement linear model-based predictive controller (lmpc) and non-linear model-based predictive controller (nmpc) strategies. a radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.an unscented kalman filter (ukf) algor...

this study contains the identification of modal dynamic properties of a 3-story large-scale steel test frame structure through shaking table measurements. shaking table test is carried out to estimate the modal properties of the test frame such as natural frequencies, damping ratios and mode shapes. among many different model structures, arx (auto recursive exogenous) model structure is used fo...

this paper presents future trends of urban wastes and their impacts on the environment of african cities using plausible mitigation scenarios. to accomplish this, an integrated dynamic model for urban waste flows was developed, tested, calibrated and validated. its parameter sensitivity was analyzed. using population projection up to 2052 with different levels of technological implementation, p...

Many practical applications including speech and audio processing, signal processing, system identification, econometrics and time series analysis involve the problem of reconstructing a dynamic system model from data observed with noise in all variables. We consider an important class of dynamic single-input single-output nonlinear systems where the system model is polynomial in observations b...

Inverse analysis of nonlinear dynamic systems is an important area of research in the field of structural health monitoring for civil engineering structures. Structural damage usually involves localized nonlinear behaviors of dynamic systems that evolve into different classes of nonlinearity as well as change system parameter values. Numerous parametric modal analysis techniques (e.g., eigensys...

................................................................................................................... ii Acknowledgements ........................................................................................................ iv 1) Introduction ................................................................................................................ 1 1.1) Original Contribu...

Inverse analysis of nonlinear dynamic systems is an important area of research in the field of structural health monitoring for civil engineering structures. Structural damage usually involves localized nonlinear behaviors of dynamic systems that evolve into different classes of nonlinearity as well as change system parameter values. Numerous parametric modal analysis techniques (e.g., eigensys...