Abstract :
Time series analysis is one of important issues in science, engineering, and so on. Up to the present statistical methods [T. Ozaki, et al. (1998)] such as AR model [T. Ozaki, et al. (1998)] and Kalman filter [S. Arimoto, (1985)] have been successfully applied, however, those statistical methods may have problems for solving highly nonlinear problems. An attempt is made to develop practical methods of nonlinear time series by introducing such soft computing techniques [L.A. Zadeh, (1965), (1973), (1993)] as chaos theory [K. Ito, (1993)], neural network [S. Chen, et al. (1989), M. Funabashi, (1992)], GMDH [A.G. Ivakhenemko, (1968), I. Hayashi, (1995)] and fuzzy modelling [(H.Nomura, et al. (1991), Y. Shi, et al. (1996)]. Using the earthquake input record obtained in Hyogo, the applicability and accuracy of the proposed methods are discussed with a comparison of those results
Keywords :
chaos; earthquakes; fuzzy systems; geophysics computing; neural nets; nonlinear systems; prediction theory; time series; chaos theory; earthquake input; fuzzy modelling; geophysics computing; group method of data handling; neural network; nonlinear systems; soft computing; time series prediction; Bridges; Chaos; Computer networks; Earthquake engineering; Neural networks; Prediction methods; Seismic measurements; Statistical analysis; Vibration control; Wind forecasting;
