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INTRODUCTION
system design framework is the brain emotional
learning-based bio-inspired control algorithm
(Kim et al. 2010).
The brain limbic system, which is responsible
for emotional reaction of humans (among other
bio-organisms), is an available candidate as a
structural control algorithm (Kim et al. 2010).
Unlike rational thought that is considered to be
objective, emotions have been considered a nega-
tive trait because emotional thought is considered
to be involuntary and there exists little conscious
control over such thought. However, scientists
have recently learned about the positive aspects
of human emotions. Moreover, for a number of
years, the emotional signal processing in the brain
limbic system has been the subject of research in
cognitive science (Picard 1997; Jamali et al. 2009;
Kim and Langari 2009). Rational thought can
be often controlled via the involuntary emotions
(Martinez-Miranda & Aldea 2005). Of special
interest is that the impact of the emotional system
on the cognitive system is far stronger than the
impact of the cognitive system on the emotional
system. For instance, one single occurrence of an
emotionally significant situation is remembered
far more vividly and for a longer period than a task
which is repeated frequently (Meystel & Albus
2002). In other words, the emotional processing
and learning are able to develop an effect that
sustained cognitive inputs are not able to achieve.
To date, great attention has been paid to the
application of artificial neural networks (ANNs) to
bio-inspired control system design. ANNs model
the synaptic connections and the Hebbian learn-
ing phenomena at the level of individual neurons
that train the input-output relations of complex
information. These linkages are being used for
decision-making when no conventional or math-
ematical input-output relations are available, i.e.,
ANNs are trained via adjusting the weights of the
various signal paths based on the error between the
desired state and the current state. ANNs that are
represented in networks of a number of neurons
inside the human brain may be used for modeling
The application of control technology to large
structures has attracted a great attention from
civil engineering because behavior of structural
systems can be modified during destructive en-
vironmental forces such as earthquakes without
significantly increasing the mass of structure
(Yao 1972; Soong 1990; Kobori et al. 1991;
Soong and Reinhorn 1993; Housner et al. 1994;
Housner et al 1997; Adeli & Saleh 1999; Spencer
and Nagarajaiah 2003; Agrawal et al. 1998; Kim
et al. 2009; Kim et al. 2010), including passive,
active, and semiactive (also called smart) systems
(Nagarajaiah & Spencer 2003; Kim et al. 2010).
Particularly, the smart control scheme has been
used most frequently to structural control system
design because it possesses the advantages of both
passive and active control systems (Spencer et al.
1997). In order to improve the performance of the
smart control system, the control algorithm for
smart control devices has to be selected carefully
(Jansen & Dyke 2000). The control algorithms
that have been used for the application of smart
control technology could be divided into two
categories: model-based and model-free control
algorithms. The typical model-based control
algorithms for implementation of smart control
systems in the field of structural engineering
might include: linear quadratic regulator, linear
quadratic Gaussian, H
∞
, etc. (Chang et al. 2008;
Lynch et al. 2008; Wang and Dyke 2008; Ping and
Agrawal 2009; Nagarajaiah and Narasimhan 2006;
Nagarajaiah et al. 2009) The model-free control
system design frameworks such as fuzzy logic
theory and artificial neural network have been
also extensively applied to smart civil structures
(Lin et al. 2007; Shook et al. 2008; Kim et al.
2009; Kim et al. 2010; Karamodin and Kazemi
2010). The reason is that the model-free control
system design framework does not require for
modeling nonlinear dynamic system of structures
equipped with complex nonlinear smart control
devices. Another new model-free smart control
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