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Nonlinear Identification of Dynamic Systems Using Neural Networks
Author(s) -
Huang ChihChieh,
Loh ChinHsiung
Publication year - 2001
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/0885-9507.00211
Subject(s) - artificial neural network , nonlinear system , nonlinear system identification , computer science , identification (biology) , point (geometry) , stochastic neural network , system identification , bridge (graph theory) , time delay neural network , control theory (sociology) , artificial intelligence , mathematics , data mining , control (management) , physics , medicine , geometry , biology , measure (data warehouse) , botany , quantum mechanics
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.