Identification of linear systems response by parametric programing

TLDR: In this article, a parametric linear programing (PLP) is used to identify the impulse response function of a linear hydrologic system from a relatively short input-output record.

Abstract: Experience indicates that in the identification of the impulse response function of a linear hydrologic system the results are extremely sensitive to minor errors in the input-output data. In particular, low-amplitude random errors in these data tend to cause severe oscillations in the response function, thereby making it often impossible to obtain a physically realizable solution by conventional methods. Artificial filtering of the input-output records may help, but since the extent of noise is seldom known a priori, one cannot be sure about the proper choice of a cutoff frequency. Such filtering also causes a loss of data at the end points of the record and is therefore undesirable when the number of data points is small. Filtering the response function itself is only effective in eliminating high-frequency oscillations, and it is far less effective when the frequency of the oscillations is relatively low. Clearly, the ultimate goal of identification is to determine a solution which optimizes the predictive capabilities of the linear model. To achieve this goal, it is not sufficient that an observed output be correctly reproduced from a given input; an equally important criterion of optimality is that the shape of the response function be physically plausible. It is shown that one way to obtain a stable and physically realizable response function from a relatively short input-output record is to use parametric linear programing. According to this approach, the problem is formulated as a multicriterion decision process under uncertainty in a manner analogous to that previously described by one of the authors in connection with the inverse problem of groundwater hydrology. Parametric programing serves as a means of generating a continuous set of alternative solutions to the identification problem together with a bicriterion function representing these alternatives. The shape of this bicriterion curve is then used as a guide by the hydrologist in selecting a particular solution when he is relying on his own value judgment. If none of the alternative solutions appears to be physically plausible at this stage, the hydrologist has a further option of imposing modality constraints to eliminate undesirable low-frequency oscillations from the response function. The method is illustrated by two examples, and the results are compared with those obtained by another approach developed previously by one of the authors.