Multiple time-varying dynamic analysis using multiple sets of basis functions

We extend recently developed algorithm that expand the time-varying parameters onto a single set of basis functions to multiple sets of basis functions. Computer simulation examples do indeed show the benefit of using multiple sets of basis functions over the single set of basis functions for cases with many switching dynamics. Furthermore, comparative simulation results of the method proposed with that of the recursive least squares (RLS) show that the method proposed achieves far superior tracking capability than the RLS. Moreover, the proposed method remains accurate even under significant noise contamination.