Parallel Wiener-Hammerstein Identification: A case study

In the field of nonlinear system identification, a parallel Wiener-Hammerstein model offers a good balance between clarity and complexity. To identify such a model, a two-step method exists, where each step has been studied in detail. A full identification scheme combining the two steps while keeping track of noise influence is the next step forward. Therefore, we applied the exisiting techniques to a specific computer-simulated case study. This document presents the total identification scheme, from start to finish, and compares different models. Errors of the final identified model are small, in the order of 0.7% relative to the output signal, when using a signal-to-noise ratio of 1%. This means the found model is able to describe the measured data accurately. The next in this research is to apply the two-step weighted approach on real-life measured data.

ISBN:9789073802940

Jaar van publicatie:2016