Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 294-302 |
| Number of pages | 9 |
| Journal | Measurement: Journal of the International Measurement Confederation |
| Volume | 87 |
| DOIs | |
| Publication status | Published - 2016 Jun 1 |
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All Science Journal Classification (ASJC) codes
- Instrumentation
- Electrical and Electronic Engineering
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System identification of smart buildings under ambient excitations. / Kim, Yeesock; Kim, Jung Mi; Kim, Young Hoon; Chong, Jowoon; Park, Hyo Seon.
In: Measurement: Journal of the International Measurement Confederation, Vol. 87, 01.06.2016, p. 294-302.Research output: Contribution to journal › Article
TY - JOUR
T1 - System identification of smart buildings under ambient excitations
AU - Kim, Yeesock
AU - Kim, Jung Mi
AU - Kim, Young Hoon
AU - Chong, Jowoon
AU - Park, Hyo Seon
PY - 2016/6/1
Y1 - 2016/6/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84962198295&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962198295&partnerID=8YFLogxK
U2 - 10.1016/j.measurement.2016.02.028
DO - 10.1016/j.measurement.2016.02.028
M3 - Article
AN - SCOPUS:84962198295
VL - 87
SP - 294
EP - 302
JO - Measurement: Journal of the International Measurement Confederation
JF - Measurement: Journal of the International Measurement Confederation
SN - 0263-2241
ER -