Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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Nonlinear system identification approach has been greatly spread among the researchers and engineers in the past few years. The neural network simulators as a non-parametric system identification approach present a robust and efficient way to simulate the nonlinear behavior of engineering systems.In this paper an artificial neural network simulator (ANNS), a general back error propagating perceptron, is used to model the nonlinear behavior of concrete structures, namely silos, when subjected to a base excitation shock and potential to be cracked and crashed. Through a parametric study, different geometries and loads of the structure are surveyed and the best network architecture for each case is obtained. The peak responses and phase delays are assumed to be the network outputs. The network is trained in a supervised manner by the data obtained from nonlinear finite element analyses. The study shows the efficiency and capability of the ANNS to model the observed nonlinear behavior.