Application Of Artificial Neural Network And Genetic Algorithm To System Identification
Grace S. Wang, Fu-Kuo Huang · Proceedings of the Joint CIB W78, W102, ICCCBE, ICCC, and DMUCE International Conference on Computing and Decision Making in Civil and Building Engineering, Montreal, Canada, 14-16 June · 2006
This paper presents a new identification technique combining the advantages of artificial neural network (ANN) and genetic algorithm (GA). In order to provide a neural network topology that can be merged into the GA identification technique developed by the author, the time history of the ground acceleration and the system parameters of a variety of SDOF systems are used as the input data of neural network, and the time history of the relative acceleration of the corresponding systems as the neural network outputs. After the training of the neural network, the network topology used to evaluate the time history of the relative acceleration of the SDOF systems will be captured. This network topology is then employed to replace the procedure for solving the governing (differential) equation when GA is used to identify the system parameters. Furthermore, this topology is used in the identification of a MDOF system subjected to single input by mode superposition technique.