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concluded that the real time recurrent learning can reduce the intensity of vibration. This implies that
the real time system identification of the dynamic characteristics of the structure can be sufficiently
done.
In this paper, another attempt was made to develop a structural vibration system that can reduce the
displacement and velocity of the structure with more efficiency. Introducing the prediction of wind
velocity at the next step, it is possible to realize a more effective control of structural vibration. In this
study, the structural characteristics are identified by using the layered-type neural network. For the
learning data, the control results given by the fuzzy control are used. Although the structural model is
very simple, the identification result is quite satisfactory.
In the development of predicting method of wind velocity, the chaos theory was useful in short-term
prediction. In the utilization of the chaos theory, it is necessary to prove the chaotic behavior of the
wind velocity record. The chaotic behavior was proven by the Lyapunov exponent analysis. The
prediction of wind velocity could be done with more accuracy by employing appropriate parameters
whose values were determined empirically. The proposed method may be promising for the structural
vibration control due to earthquake excitations.
The authors would like to acknowledge Osaka municipal office and Hitachi Zosen Corp. for providing
the data of wind velocity measured at Osaka bay.
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