  With the rapid development of deep learning technology, more and more
researchers apply it to gravitational wave (GW) data analysis. Previous studies
focused on a single deep learning model. In this paper we design an ensemble
algorithm combining a set of convolutional neural networks (CNN) for GW signal
recognition. The whole ensemble model consists of two sub-ensemble models. Each
sub-ensemble model is also an ensemble model of deep learning. The two
sub-ensemble models treat data of Hanford and Livinston detectors respectively.
Proper voting scheme is adopted to combine the two sub-ensemble models to form
the whole ensemble model. We apply this ensemble model to all reported GW
events in the first observation and second observation runs (O1/O2) by
LIGO-VIRGO Scientific Collaboration. We find that the ensemble algorithm can
clearly identify all binary black hole merger events except GW170818. We also
apply the ensemble model to one month (August 2017) data of O2. There is no
false trigger happens although only O1 data are used for training. Our test
results indicate that the ensemble learning algorithms can be used in real-time
GW data analysis.
