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ABSTRACT: A new IRED (interval random early detection) congestion control algorithm is proposed for network congestion avoidance and
resource management. Different to the traditional AQM (active queue management) algorithms, the control parameters of IRED
are not configured statically, and is setting as a parameter interval according to the changes of network environment. By
the interval parameter design, the IRED alleviates the tuning difficulty of RED (random early detection) and shows a robust
performance than RED under varying network conditions. It is proved that the stability and stability margin of the IRED control
system can be guaranteed. A systematic design method for the configuration of parameter interval is proposed. Simulation studies
show the proposed IRED algorithm achieves a robust control performance in varying network environment, which is superior to
the RED and Gentle-RED algorithm.
KeywordsAQM–congestion control–IRED–RED
International Journal of Control Automation and Systems 04/2012; 9(1):98-103. · 0.75 Impact Factor
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ABSTRACT: One of the main problems for effective control of a minimally invasive surgery (MIS) is the imprecision that caused by hand
tremor. In this paper, a novel adaptive filter, the least squares support vector machines adaptive filter (LS-SVMAF), is proposed
to overcome this problem. Compared with traditional methods like multi layer perceptron (MLP), LS-SVM shows a superior performance
of nonlinear modeling with small scale of data set or high dimensional input space. With the LS-SVMAF, we can model and predict
the hand tremor more effectively and improve the precision and reliability in the master–slave robotic system for microsurgery.
Simulation results demonstrate the effectiveness of the proposed filter and its superior performance over its competing rivals.
KeywordsMinimally invasive surgery (MIS)–Least squares support vector machines (LS-SVM)–Multi layer perceptron (MLP)–Hand tremor
04/2012; 2(1):37-47.
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Appl. Soft Comput. 01/2011; 11:5315-5329.
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ABSTRACT: A novel probabilistic fuzzy control system is proposed to treat the congestion avoidance problem in transmission control protocol
(TCP) networks. Studies on traffic measurement of TCP networks have shown that the packet traffic exhibits long range dependent
properties called self-similarity, which degrades the network performance greatly. The probabilistic fuzzy control (PFC) system
is used to handle the complex stochastic features of self-similar traffic and the modeling uncertainties in the network system.
A three-dimensional (3-D) membership function (MF) is embedded in the PFC to express and describe the stochastic feature of
network traffic. The 3-D MF has extended the traditional fuzzy planar mapping and further provides a spatial mapping among
“fuzziness-randomness-state”. The additional stochastic expression of 3-D MF provides the PFC an additional freedom to handle
the stochastic features of self-similar traffic. Simulation experiments show that the proposed control method achieves superior
performance compared to traditional control schemes in a stochastic environment.
Journal of Control Theory and Applications 01/2009; 7(1):29-34.
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ABSTRACT: In this paper, a type-2 fuzzy switching control system is proposed for a biped robot, which includes switched nonlinear system modeling, type-2 fuzzy control system design, and a type-2 fuzzy modeling algorithm. A new switched system model is proposed to represent the continuous-time dynamic and discrete-event dynamic of a walking biped as a whole, which is helpful to analyze the closed-loop stability of the biped locomotion. A type-2 fuzzy switching control system is proposed for the switched system model to guarantee the gait stability and to achieve a robust control performance with a simplified control scheme. Finally, we propose a new fuzzy c-mean variance algorithm for the type-2 fuzzy system modeling to capture the variance of each clustering means, which can translate random uncertainties of original data into rule uncertainties. Simulation results are reported to show the performance of the proposed control system model and algorithms.
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) 12/2007; · 2.01 Impact Factor
