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Applying Self-Recursive Neural Network Prediction to Compensate for the Delay of Real-Time Substructure Experiment

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2 Author(s)
Tu Jianwei ; Hubei Key Lab. of Roadway Bridge & Struct. Eng., Wuhan Univ. of Technol., Wuhan, China ; Zhang Kaijing

Self-recursive neural network is used to predict structural dynamic responses and compensate for the delay of the hydraulic servo actuator which is the major problem of real-time substructure experiment and cause a direct influence on the stability and veracity. In this paper, the experimental setup is established consisting of D-space real-time simulator, hydraulic actuator, measuring system, data collecting system and measure the value of the delayed time of actuator. On the basis of that, the self-recursive neural network is trained and used to compensate for the delay, so that the numerical model and the experimental substructure can be coordinated and transfigured. Finally, a real-time substructure experiment is performed on a three-storied structure under seismic excitation, which proves the validity of this method.

Published in:

Natural Computation, 2009. ICNC '09. Fifth International Conference on  (Volume:1 )

Date of Conference:

14-16 Aug. 2009