Time Series Earthquake
Time Series Prediction of Earthquake Input by using Soft Computing
Hitoshi FURUTA, Yasutoshi NOMURA Department of Informatics, Kansai University, Takatsuki, Osaka569-1095, Japan nomura@sc.kutc.kansai-u.ac.jp
Abstract
Time series analysis is one of important issues in science, engineering, and so on. Up to the present statistical methods[1] such as AR model[2] and Kalman filter[3] have been successfully applied, however, those statistical methods may have problems for solving highly nonlinear problems. In this paper, an attempt is made to develop practical methods of nonlinear time series by introducing such Soft Computing techniques[4][5][6] as Chaos theory[7], Neural Network[8][9], GMDH[10][11] and fuzzy modeling[12][13]. Using the earthquake input record obtained in Hyogo, the applicability and accuracy of the proposed methods are discussed with a comparison of those results.
In this paper, an attempt is made to develop practical prediction methods of earthquake input, which behaves irregularly time to time, by introducing such so-called soft computing techniques as Chaos theory (Ito,1993[7], Takens, 1981[14][15][16], Iokibe, 1994[17], Sakawa at all, 1998[18]) Neural Network (Chen at all, 1989[8], Funabashi, 1992[9]) and GMDH (Group Method of Data Handling)(Ivakhenemko, 1968[10], Hayashi, 1985[11]). Many researches have revealed that the Chaos theory is useful in dealing with complex systems, Neural Network is applicable to various problems like pattern recognition and function approximation, and GMDH can analyze highly nonlinear systems which have a few input and output variables. Numerical examples are presented to illustrate the applicability of the proposed methods, and to compare the characteristics of those methods.
1.Introduction
In this study, the prediction of external force such as earthquakes and wind loads is employed to discuss the accuracy and efficiency of the prediction methods, because of the importance of its prediction from the
Hitoshi FURUTA, Yasutoshi NOMURA Department of Informatics, Kansai University, Takatsuki, Osaka569-1095, Japan nomura@sc.kutc.kansai-u.ac.jp
Abstract
Time series analysis is one of important issues in science, engineering, and so on. Up to the present statistical methods[1] such as AR model[2] and Kalman filter[3] have been successfully applied, however, those statistical methods may have problems for solving highly nonlinear problems. In this paper, an attempt is made to develop practical methods of nonlinear time series by introducing such Soft Computing techniques[4][5][6] as Chaos theory[7], Neural Network[8][9], GMDH[10][11] and fuzzy modeling[12][13]. Using the earthquake input record obtained in Hyogo, the applicability and accuracy of the proposed methods are discussed with a comparison of those results.
In this paper, an attempt is made to develop practical prediction methods of earthquake input, which behaves irregularly time to time, by introducing such so-called soft computing techniques as Chaos theory (Ito,1993[7], Takens, 1981[14][15][16], Iokibe, 1994[17], Sakawa at all, 1998[18]) Neural Network (Chen at all, 1989[8], Funabashi, 1992[9]) and GMDH (Group Method of Data Handling)(Ivakhenemko, 1968[10], Hayashi, 1985[11]). Many researches have revealed that the Chaos theory is useful in dealing with complex systems, Neural Network is applicable to various problems like pattern recognition and function approximation, and GMDH can analyze highly nonlinear systems which have a few input and output variables. Numerical examples are presented to illustrate the applicability of the proposed methods, and to compare the characteristics of those methods.
1.Introduction
In this study, the prediction of external force such as earthquakes and wind loads is employed to discuss the accuracy and efficiency of the prediction methods, because of the importance of its prediction from the
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