Abstract
A multiscale system identification methodology is presented and discussed, that extends, in a systematic way, the classical board of single-scale system identification tools to a multiscale context. The proposed approach is built upon a wavelet-based multiscale decomposition in a receding horizon sliding window that always includes the last measured values, in order to make it adequate for on-line use. Several examples are presented that illustrate different features of the multiscale modeling framework, such as its improved ability to perform prediction in output variables having most of its energy concentrated at intermediate or coarser time scales when compared to input variables, and its intrinsic smoothing capability.
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