Identification of piecewise linear parameters of regression models of non-stationary deterministic systems

We consider the problem of identifying unknown nonstationary piecewise linear parameters for a linear regression model. A new algorithm is proposed that allows, in the case of a number of assumptions on the elements of the regressor, to provide an estimate of unknown non-stationary parameters. We analyze in detail the case with two unknown parameters, which makes it possible to understand the main idea of the proposed approach. We also consider a generalization to the case of an arbitrary number of parameters. We give an example of computer simulation that illustrates the efficiency of the proposed approach.