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Fig. 5 4ttractor (time lag=l) Fig. 6 Result of Lyapunov Exponent Analysis
When a time series shows a chaotic behavior it may follow a deterministic rule Then it is possible to
predict the near future behavior of the time series with the aid of chaos theory Here it is attempted to
predict the wind velocity given by a time series record using Local Fuzzy Reconstruction Method
(LJFRM) (lokibe et al, 1994) and Dtermimstic Nonlinear Prediction Method using Neighborhood s
Difference (NDM) (Sakawa et al 1998) Fig 7 and Fig 8 show prediction results by LFRM and
NDM
Application Example
In the proposed system the structural characteristics are determined by the neural computing and the
wind velocity at the next step is predicted by the chaos theory Then the wind load at the next step is
calculated through the neural computing by using the vibration states and control force at the previous
step as the input data Introducing the wind load calculated, fuzzy active control is implemented The
procedure can provide the control force at a step before than the usual fuzzy control This enables to
realize a more flexible and effective vibration control
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Fig. 7 Prediction Result by LFRM
Fig. 8 Prediction Result by NDM
Figs 9 thorough 11 present the relative displacements of the cases without control, with fuzzy control
and with the proposed system, respectively Figs 12 through 14 depict the changes of relative
velocity TABLE 4 shows the mean displacement and mean velocity It is seen that the proposed
system can provide the best result among the three methods to reduce the vibration
CONCLUSIONS
In order to realize a more accurate and practical control for structural vibration of structures, it is