Hybrid testing method based on online neural network algorithm
WANG Tao1,2，ZHAI Xuheng2，Meng Liyan2, WANG Zhen3
1.Institute of Engineering Mechanics, China Earthquake Administration Key Laboratory of Earthquake Engineering and Engineering Vibration of China Earthquake Administration, Harbin 150080, China;
2. School of Civil Engineering, Heilongjiang University of Science ＆ Technology, Harbin 150022, China;
3. School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
Hybrid testing is an advanced structure seismic experimental method that combines numerical simulation and physical testing and is increasingly being recognized by researchers. One of the challenging issues is how to model the numerical substructure with strong nonlinearity. An online learning neural network algorithm (NN) is proposed based on conventional offline NN algorithm and applied in the hybrid testing to online predict the restoring force for the numerical structure. Weights and thresholds of the novel algorithm can be updated using recursive form only based on the current step systematic inputs and observations. Numerical simulations of hybrid testing based the online and the offline NN algorithms are conducted for a two freedom nonlinear system. The results show the new online NN hybrid testing method has better adaptation, computational efficiency and prediction accuracy for restoring force, compared with offline NN hybrid testing method. In general, the online NN hybrid testing method can effectively improve the accuracy of hybrid testing results.
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