Conference Program of WCICA 2012

WCICA 2012
Book of Abstracts: Sunday Sessions
plied even when the abnormal neuron population is disturbed by heavy
noise.
◮ SuB08-5 17:10–17:30
Solving Graph Vertex Coloring Problem with Microfluidic DNA Computer,
pp.5061–5065
Niu, Ying
Zhang, Xuncai
Cui, Guangzhao
Zhengzhou Univ. of Light Industry
Zhengzhou Univ. of Light Industry
Zhengzhou Univ. of Light Industry
The hugely storing information ability, parallel computing ability and
lower computing energy cost make DNA computing to be a perfect
computing paradigm. Nowadays it has been used to solve various
computationally hard problems. In order to improve its reliability and
simplify operations, microfluidic chips support an effective way to realize
an automatable and universal DNA computer. In this paper we
introduce microfluidic logic operators, simple fluidic switches and memory.
Furthermore, the use of electronic fluidic control components in
microfluidic systems will be demonstrated in such way as to perform
dynamic operations and programming. Finally a proposal for an actual
fluidic computer will be made which solves the graph vertex coloring
problems.
◮ SuB08-6 17:30–17:50
Action potential initial mechanism control of a minimum model response
to constant and sinusoidal stimulus, pp.4948–4952
Li, Huiyan
Wang, Jiang
Jin, Qitao
Deng, Bin
Wei, Xile
Che, Yan-Qiu
Tianjin Univ. of Tech. & Education
Tianjin Univ.
Tianjin Univ.
tianjin Univ.
Tianjin Univ.
Tianjin Univ. of Tech. & Education
Neuron encodes the information inputs from the dendrites by generating
different firing patterns. The different firing patterns result from
different action potential initial dynamic mechanisms. In this paper, we
adopt a minimum neuron model, design the Wash-out filter from a physiological
view, and achieve the transition between different action potential
initial dynamic mechanisms. Finally, we demonstrate the physiological
basis of Wash-out filter, which is affecting the result of competition
between currents with different dynamics in the sub-threshold potential.
SuB09 15:50–17:50 Room 311A
Invited Session: Data-based control, modeling and optimization
Chair: Liu, Derong
Co-Chair: He, Haibo
Chinese Acad. of Sci.
Univ. of Rhode Island
◮ SuB09-1 15:50–16:10
Data-Based Approach for the Control of a Class of Nonlinear Affine
Systems, pp.2722–2727
Wang, Zhuo
Liu, Derong
Univ. of Illinois at Chicago
CASIA
In this paper, a data-based output feedback control method is developed
for a class of nonlinear affine systems. This method requires little
priori knowledge about the system. It does not need to know or to build
the mathematical model of the system. We apply a fast sampling technique
to measure the output signal, which contains information about
the plant. The zero-order hold (ZOH) and the control switch techniques
are also applied for information collection. Then, the feedback gain matrix
is calculated and adjusted according to these sampled data. Computer
simulation results demonstrate the feasibility of this data-based
control method.
◮ SuB09-2 16:10–16:30
Data-Driven Learning and Control with Multiple Critic Networks,
pp.523–527
He, Haibo
Ni, Zhen
Zhao, Dong-bin
Univ. of Rhode Island
Univ. of Rhode Island
Inst. of automation
Abstract - In this paper, we extend our previous work of a three-network
adaptive dynamic programming design [1] to be a multiple critic networks
design for online learning and control. The key idea of this approach
is to develop a hierarchical internal goal representation to facilitate
the online learning with detailed and informative internal value
signal representations. We present our learning architecture in detail,
and also demonstrate its performance on the popular cart-pole balancing
benchmark. Simulation results demonstrate the effectiveness of our
approach. We also present discussions of further research directions
along this topic.
◮ SuB09-3 16:30–16:50
Data-driven Model Free Adaptive Control for Block-Connected Systems,
pp.2827–2832
Zhu, Yuanming
Hou, Zhongsheng
Jin, Shangtai
Beijing Jiaotong Univ.
Beijing Jiaotong Univ.
Beijing Jiaotong Univ.
Data-driven model free adaptive control (MFAC) is presented for three
kinds of block-connected discrete-time nonlinear systems, describing
by cascaded connection, parallel connection and feedback connection.
The proposed data-driven MFAC means that the controller is designed
merely by the measured input-output data of the controlled system without
any explicit or implicit use of the plant model. The stability of the
data-driven MFAC is guaranteed by rigorous theoretical analysis and
the effectiveness is verified by simulation results.
◮ SuB09-4 16:50–17:10
Integration of Fuzzy Controller with AdaptiveDynamic Programming,
pp.310–315
Zhu, Yuanheng
Zhao, Dong-bin
He, Haibo
Inst. of Automation Chinese Acad. of Sci.
Inst. of automation
Univ. of Rhode Island
Adaptive dynamic programming (ADP) is an effective method for learning
while fuzzy controller has been put into use in many applications
because of its simplicity and no need of accurate mathematic modeling.
The combination of ADP and fuzzy control has been studied a
lot. Before this paper, we have studied using ADP to learn the fuzzy
rules of a Monotonic controller, which shows good performance. In this
paper, a hyperbolic fuzzy model is adopted to make an improvement.
In this way, both membership function and fuzzy rules are learned.
With ADP algorithm, fuzzy controller has the capacity of learning and
adapting. Simulations on a single cart-pole plant and a rotational inverted
pendulum are implemented to observe the performance, even with
uncertainties and disturbances.
◮ SuB09-5 17:10–17:30
Learning Control of a Bioreactor System Using Kernel-based Heuristic
Dynamic Programming, pp.316–321
Lian, Chuanqiang
Xu, Xin
National Univ. of Defense Tech.
National Univ. of Defense Tech.
To solve the learning control problem of a bioreactor system, a novel
framework of heuristic dynamic programming (HDP) with sparse kernel
machines is presented, which integrates kernel methods into critic
learning of HDP. As a class of adaptive critic designs (ACDs), HDP
has been used to realize online learning control of dynamical systems,
where neural networks are commonly employed to approximate the value
functions or policies. However, there are still some difficulties in the
design and implementation of HDP such as that the learning efficiency
and convergence of HDP greatly rely on the empirical design of the critic
and so on. In this paper, by using the sparse kernel machines, Kernel
HDP (KHDP) is proposed and its performance is analyzed both theoretically
and empirically. Due to the representation learning and nonlinear
approximation ability of sparse kernel machines, KHDP can obtain better
performance than previous HDP method with manually designed
neural networks. Simulation results demonstrate the effectiveness of
the proposed method.
SuB10 15:50–17:50 Room 311B
Invited Session: Wireless Sensor Networks
Chair: Xiao, Wendong
Co-Chair: Wu, Jian Kang
Univ. of Sci. & Tech. Beijing
Graduate Univ., Chinese Acad. of Sci.
◮ SuB10-1 15:50–16:10
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