<title>Post-processing for JPEG 2000 image coding using recursive line filtering based on a fuzzy control model</title>

A new fuzzy robust control strategy for the nonlinear supply chain system in the presence of lead times is proposed. Based on Takagi-Sugeno fuzzy control system, the fuzzy control model of the nonlinear supply chain system with lead times is constructed. Additionally, we design a fuzzy robust H∞ control strategy taking the definition of maximal overlapped-rules group into consideration to restrain the impacts such as those caused by lead times, switching actions among submodels, and customers’ stochastic demands. This control strategy can not only guarantee that the nonlinear supply chain system is robustly asymptotically stable but also realize soft switching among subsystems of the nonlinear supply chain to make the less fluctuation of the system variables by introducing the membership function of fuzzy system. The comparisons between the proposed fuzzy robust H∞ control strategy and the robust H∞ control strategy are finally illustrated through numerical simulations on a two-stage nonlinear supply chain with lead times.

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Study on Environmental Management Based on Fuzzy Control and Markov Chain

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.113-116.994 ◽

2010 ◽

Vol 113-116 ◽

pp. 994-997

Author(s):

Jin Ying Li ◽

Ting Wang ◽

Jin Chao Li

Keyword(s):

Environmental Management ◽

Markov Chain ◽

Fuzzy Control ◽

Dynamic Environment ◽

Environmental Policies ◽

Control Model ◽

Emission Behavior

To improve the implementation efficiency of environmental policies, the paper put forward a dynamic supervision method based on Markov chain and fuzzy control. With Markov chain the enterprises’ Emission behavior can be forecasted. And with fuzzy control model, analyze the current emissions behavior and the recent changes in the emissions records. Combined the two results, Supervisors decide their dynamic environment supervision track. The sample analyses show that the method is practical.

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Size Prediction Control Modeling in Cylindrical Grinding

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.154-155.977 ◽

2010 ◽

Vol 154-155 ◽

pp. 977-980

Author(s):

Ning Ding ◽

Shi Qiang Ma ◽

Yu Mei Song ◽

Long Shan Wang

Keyword(s):

Fuzzy Control ◽

Model Simulation ◽

Size Control ◽

Control Model ◽

Elman Neural Network ◽

Cylindrical Grinding ◽

Control Subsystem ◽

Control Precision ◽

Prediction And Control ◽

Prediction Control

A dynamic size control model during cylindrical grinding is built. The model consists of Elman neural network, fuzzy control subsystem and deformation optimal adaptive control subsystem. To improve the size prediction accuracy, the first and the second derivative of the actual amount removed from the workpiece are added into the Elman network input; To self-adapt and adjust the quantification factor and scale factor in the fuzzy control, the flexible factor is introduced to the fuzzy control model. Simulation and experiment verify that the developed prediction control model is feasible and has high prediction and control precision.

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