Nonlinear identification of dynamic systems using neural networks

A neural-network-based method is proposed for the modeling and identification of a discrete-time nonlinear hysteretic system during strong earthquake motion. The learning or modeling capability of multilayer neural networks is explained from the mathematical point of view. The main idea of the proposed neural approach is explained, and it is shown that a multilayer neural network is a general type of NARMAX model and is suitable for the extreme nonlinear input-output mapping problems. Numerical simulation of a three-story building and a real structure (a bridge in Taiwan) subjected to several recorded earthquakes are used here to demonstrate the proposed method. The results illustrate that the neural network approach is a reliable and feasible method.