This study utilizes back propagation(BP)neural networks to fit the displace-ment-force system of nonlinear structures.The trained neural network is then used as a physical substructure in conjunction with a numerical substructure to solve the problem,e-liminating the need for real-time data exchange between physical and numerical substruc-tures.By iteratively training sample data,this approach continually approaches the real response,overcoming the issue of requiring a large number of training samples for neural networks.The feasibility of the proposed method was verified through numerical simulation of hybrid experiments on two degrees of freedom nonlinear structures.An actual engineering framework was chosen as the object for hybrid experiments,and a seismic iso-lation pad was selected as the experimental substructure for numerical simulation,further confirming the effectiveness of the method.
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