Osamu Maruyama

Kalman Filtering of Versatile Restoring Systems

This paper presents an identification method on a hysteretic restoring system by appling the Extended Kalman filter incorporated with a weighted global iteration. By this method, a nonlinear versatile model applied to general hysteretic system may be identified in terms of the model’s parameters, at the stage of their convergency to optimal ones after weighted global iterations of the Extended Kalman filtering. The identification method is demonstrated for the estimation of response characteristics of a near surface ground subjected to a strong earthquake ground motion. Also, the justification of the method is investigated on numerically simulated data on responses of a known’ degrading system.

Identification of Autoregressive Process Model by the Extended Kalman Filter

Much attention has been paid by many researchers to stochastic time domain models such as autoregressive and moving average (ARMA), autoregressive (AR) or moving average (MA) models that can be used in the idealization of earthquake ground motions, since by these models not only sample input waves but also response waves may be recursively simulated and consequently it has been found that they are effective models used in the field of stochastic dynamics [1]~ [9].