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 parameterswhich are affected by measurement noise and error contained in prescribed parameters. The method isbased on sensitivities of the identified parameters with respect to model parameter error and measurementnoise along with the law of error propagation. By conducting numerical experiments on simple models, itis confirmed that the confidence region coincides well with the results of numerical experiments.Furthermore, the optimum arrangement of sensor locations is evaluated when uncertainty exists inprescribed parameters, based on the concept that square sum of coefficients of variations of identifiedresults attains minimum. Good agreement of the theoretical results with those of numerical simulationconfirmed validity of the theory.