This paper proposes a nonlinear autoregressive moving average (NARMA) model for use in system identification (SI) of high performance smart buildings under ambient excitations. The NARMA model is implemented by including the cross terms of output signals to a linear autoregressive moving average (LARMA) time series model. To demonstrate the effectiveness of the proposed NARMA approach, a three-story building equipped with smart control devices is investigated under a variety of ambient excitations. To access the robustness of the proposed model, it is tested under various levels of measurement noises. It is demonstrated from the extensive simulations that the proposed NARMA model is effective in predicting the ambient vibration responses of the high performance smart buildings with severe measurement noises.
|Number of pages||9|
|Journal||Measurement: Journal of the International Measurement Confederation|
|Publication status||Published - 2016 Jun 1|
Bibliographical noteFunding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the South Korea government (MEST) (No. 2011-0018360 ).
© 2016 Elsevier Ltd. All rights reserved.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering