System identification techniques

System Identification Techniques. In system identification, the (nonlinear) resi pnses of the outputs of a system to the input signals are approximated by a linear model. The parameters in this linear model are determined by minimizing a criterion function that is based on some difference between the input-output data and the responses predictedv by the model. Several model structures can be chosen and depending on this structure, different criteria can be used (l ,IX) System identification is mainly used as a technique to determine models from measured input-output data of processes, but can also be used to determine compact models for complex physical models The input-output data is then obtained from simulations of the physical model. 

FIGURE 16.5 Saccadic eye movement in response to a 15° target movement. Solidline is the prediction of the saccadic eye movement model with the final parameter estimates computed using the system identification techniques. Dots are the data. (From Enderle, J.D. and Wolfe, J.W. 1987. IEEE Trans. Biomed. Eng. 34 43-55. With permission.)... 

Cooper, J. E. Comparison of some time domain system identification techniques using approximate data correlations. International Journal of Analytical and Experimental Modal Analysis 4(2) (1989), 51-57. 

The problem of damage detection is often solved by a system identification technique past research has aimed primarily at implementing efficient solutions for each relevant inverse problem, where the efficiency is usually related to the computational aspects. 

The locus taken by the roots of the characteristic differential equation of the load cell as the applied mass changes can be determined by automatic system identification techniques. Such a locus is illustrated in Fig. 7.5, and the roots of the compensating filter need to follow it. For each value of mass there is a corresponding final output of the compensating filter once oscillation has ceased. The trick is to make the parameters of the filter vary with its own output as dictated by the locus. 

F. Kozin, H-G. Natke, System identification techniques. Struct. Safety, Vol. 3,... 

System Identification Techniques Applied to Conducting Polymer... 

The examples presented below employ linear system identification techniques in which superposition and scaling are assumed. These enable conversion between the frequency and time-domain models. Nonlinearities in conducting polymers can arise from significant potential-dependent ionic and electronic conductivities, for example. In cases where nonlinearities are significant, it may nevertheless be appropriate to apply linear techniques over certain ranges of voltage, strain, or charge state where response is effectively linear. 

The deterministic system behavior is described by the four system matrices A, B, G, and J. The process noise vectorW i S and measurement noise vector V[,t] account for unknown excitation sources and modeling errors. In addition, the measurement noise vector V[,t] accounts for measurement errors. As an alternative to models based on first principles, models can be directly identified from experimental vibration data using system identification techniques see, e.g.. Van Overschee and De Moor (1993) and Reynders and De Roeck (2008). 

Monitoring the evolution of the features over time allows, in principle, to detect structural damage. In practice, however, this needs to be applied with care because of two reasons. Firstly, many features cannot be measured directly, but they have to be estimated from measured data using system identification techniques. Modal characteristics, for instance, can be estimated from vibration response data such as accelerations or strains, but this introduces estimation errors (Reynders et al. (2008) Reynders 2012). Secondly, nearly all features are not only sensitive to structural damage but also to changes in temperature, relative humidity, wind speed, operational loading, etc. This means that both the accuracy of the estimated features and the environmental and operational influences should be accounted for. 

FIGURE 25.14 (a) Dynamic overshoot saccade of 8° (b) glissadic overshoot saccade of 8 and (c) normal -12 sac-cade. The first two lines of graphs are the active state tension and neural input calculated from the parameter estimation. Also shown are the model predictions using the parameter estimates from the system identification technique for displacement, velocity and acceleration, and the data (red line is data and blue line is the model predictions). 

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