Confidence region of identified parameters and optimal sensor locations based on sensitivity analysis

This paper presents a computational method for a confidence region of identified parameters<br />which are affected by measurement noise and error contained in prescribed parameters. The method is<br />based on sensitivities of the identified parameters with respect to model parameter error and measurement<br />noise along with the law of error propagation. By conducting numerical experiments on simple models, it<br />is confirmed that the confidence region coincides well with the results of numerical experiments.<br />Furthermore, the optimum arrangement of sensor locations is evaluated when uncertainty exists in<br />prescribed parameters, based on the concept that square sum of coefficients of variations of identified<br />results attains minimum. Good agreement of the theoretical results with those of numerical simulation<br />confirmed validity of the theory.