Identification of nonstationary stochastic systems using parallel estimation schemes

The multiple-model technique is proposed for the purpose of identification of nonstationary stochastic systems. Its most important feature is the parallel structure of computation-not one but several identification algorithms characterized by different adaptation gains are run in parallel and combined appropriately. The paper shows that the proposed scheme can significantly decrease sensitivity of the identification algorithm to the ‘rate of nonstationarity’ of the analyzed system (or alternatively-to the present choice of design parameters such as adaptation gains, forgetting factors, etc.).