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Wind Velocity (t J
Relative Displacement (tj
Relative Velocity (t n)
Wind Velocity (t n_,)
Relative Displacement^)
Relative Velocity (t n.,)
When applying this system to the structural
control, it is assumed that the external force,
the velocity and displacement are measured
and normalized by 5% of each maximum
value.
Fig. 1 Learning Process of Recurrent Neural Network
Err
6QOE-02
500E-02
4DOE-02
First, 1,000 times learning is executed by
using 100 wind velocity data obtained at
every 0.05 second as the input data. The
degree of sigmoid function, learning
coefficient, and the node number of
intermediate layer are supposed to be 1.0,
0.01, and 10, respectively. Fig. 2 shows the
convergence process of the square error in
learning.
300E-02
200E-02
1OQE-02
ODOE+00
Fig.2
200 400 600
800 1000
TineStep(005sec)
Convergence Process of Square Error
The calculated displacement and velocity are presented in Fig. 3 and Fig. 4, and the maximum velocity
and displacement are presented in TABLE 1 and the average velocity and displacement are presented
in TABLE 2. In Fig. 3 and Fig. 4, the dotted line shows the displacement or velocity without control,
and the solid line shows the results obtained by the structural control using the recurrent neural
network.
0.06
0.04
0.02
0
-002
-0.04
-0.06
.
^
Jblg. 3 .Displacement alter Learning
-.
Fig.4 Velocity after Learning