Abstract
System identification deals with the problems of building mathematical models of the systems based on observed input/output data from the systems. Optimal experimental design for system identification to extract the maximum information about the system has been extensively investigated, such as optimal design of input, sampling intervals, pre-filters, etc. In system identification, there are model structure determination and parameter estimation steps, and the optimality criteria for both objects are sometimes conflicting. This leads the necessity of the compromises in the design, i.e., the tradeoffs between the performances in these two steps should be introduced. We investigate in this paper the optimal input design for identification of linear stochastic systems from the viewpoint of multi-objective optimization problem. The Pareto-optimal set of inputs is derived and how it is used in system identification is discussed.
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