Publications Details
Simulation of structural response using a recurrent radial basis function network
System behaviors can be accurately simulated using artificial neural networks (ANNs), and one that performs well in simulation of structural response is the radial basis function network. A specific implementation of this is the connectionist normalized linear spline (CNLS) network, investigated in this study. A useful framework for ANN simulation of structural response is the recurrent network. This framework simulates the response of a structure one step at a time. It requires as inputs some measures of the excitation, and the response at previous times. On output, the recurrent ANN yields the response at some time in the future. This framework is practical to implement because every ANN requires training, and this is executed by showing the ANN examples of correct input/output behavior (exemplars), and requiring the ANN to simulate this behavior. In practical applications, hundreds or, perhaps, thousands, of exemplars are required for ANN training. The usual laboratory and non-neural numerical applications to be simulated by ANNs produce these amounts of information. Once the recurrent ANN is trained, it can be provided with excitation information, and used to propagate structural response, simulating the response it was trained to approximate. The structural characteristics, parameters in the CNLS network, and degree of training influence the accuracy of approximation. This investigation studies the accuracy of structural response simulation for a single-degree-of-freedom (SDF), nonlinear system excited by random vibration loading. The ANN used to simulate structural response is a recurrent CNLS network. We investigate the error in structural system simulation.