Earthquake Engineering Research - HKU Libraries - The University ...

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No control
RNN
No control
RNN
TABLE 1. MAXI1VIUM VALUES
Velocity
4 8143 fm/s)
43197(m/s)
Displacement
0 6353 (m)
0 6277 (m)
TABLE 2. AVERAGE VALUES
Velocity
0 4733 (m/s)
0 1215 (m/s)
Displacement
0 0442 (m)
0 0236 (m)
PREDICTIVE FUZZY CONTROL FOR STRUCTURAL VIBRATION
Fuzzy Active Vibration Control
In essential the fuzzy control is employed here as a basic control method m which fuzzy rules are tuned by the
prediction results of wind velocity As a fuzzy control rule the following If-Then rules are used
If (wind load) and (relative velocity), then (control force)
where the antecedent and consequent parts are defined m terms of seven membership functions
respectively Representative rules are presented m TABLE 3
TABLE 3. REPRESENTATIVE RULES
A
NB
NM
NS
ZR
PS
PM
PB
NB NM NS ZR PS PM PB
PB
PB
PB
PB
PB
PB
PB
PM
NB
NM
NS
PS
PS
PM
PB
NB
NM
NS
ZR MS NM
PS
PM
PB
NB
NB
NB
NB
NB
NB
NB
Identification of Structural Characteristics Using Neural Network
Structural characteristics are identified by using a layered type neural network The identification of structural
characteristics is considered as one of approximation of nonlinear functions Using the data from 5001 to 10000
steps, wind load and control force are learned As output values the relative wind velocity at the next step is
obtained bv implementing the neural computing with values of wind load, relative structural displacement and
velocity at the previous step, control force at the previous step as input variables For learning, back-propagation
method is employed
Time Series Prediction by Chaos Theory
If the data sampled is chaotic the behavior is regard to be governed by a deterministic rule Then, if a
non-linear deterministic rule is estimated, it is able to predict the data of near future until the
deterministic rule does not work due to the sensitivity to the initial state Fig 5 and Fig 6 show the
attractor and the result of Lyapunov exponent analysis