In this paper a PID Fuzzy-Neural controller (FNC) is designed as an Active Queue Management (AQM) in internet routers to improve the performance of Fuzzy Proportional Integral (FPI) controller for congestion avoidance...In this paper a PID Fuzzy-Neural controller (FNC) is designed as an Active Queue Management (AQM) in internet routers to improve the performance of Fuzzy Proportional Integral (FPI) controller for congestion avoidance in computer networks. A combination of fuzzy logic and neural network can generate a fuzzy neural controller which in association with a neural network emulator can improve the output response of the controlled system. This combination uses the neural network training ability to adjust the membership functions of a PID like fuzzy neural controller. The goal of the controller is to force the controlled system to follow a reference model with required transient specifications of minimum overshoot, minimum rise time and minimum steady state error. The fuzzy membership functions were tuned using the propagated error between the plant outputs and the desired ones. To propagate the error from the plant outputs to the controller, a neural network is used as a channel to the error. This neural network uses the back propagation algorithm as a learning technique. Firstly the parameters of PID of Fuzzy-Neural controller are selected by trial and error method, but to get the best controller parameters the Particle Swarm Optimization (PSO) is used as an optimization method for tuning the PID parameters. From the obtained results, it is noted that the PID Fuzzy-Neural controller provides good tracking performance under different circumstances for congestion avoidance in computer networks.展开更多
文摘In this paper a PID Fuzzy-Neural controller (FNC) is designed as an Active Queue Management (AQM) in internet routers to improve the performance of Fuzzy Proportional Integral (FPI) controller for congestion avoidance in computer networks. A combination of fuzzy logic and neural network can generate a fuzzy neural controller which in association with a neural network emulator can improve the output response of the controlled system. This combination uses the neural network training ability to adjust the membership functions of a PID like fuzzy neural controller. The goal of the controller is to force the controlled system to follow a reference model with required transient specifications of minimum overshoot, minimum rise time and minimum steady state error. The fuzzy membership functions were tuned using the propagated error between the plant outputs and the desired ones. To propagate the error from the plant outputs to the controller, a neural network is used as a channel to the error. This neural network uses the back propagation algorithm as a learning technique. Firstly the parameters of PID of Fuzzy-Neural controller are selected by trial and error method, but to get the best controller parameters the Particle Swarm Optimization (PSO) is used as an optimization method for tuning the PID parameters. From the obtained results, it is noted that the PID Fuzzy-Neural controller provides good tracking performance under different circumstances for congestion avoidance in computer networks.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.60674015)上海市自然科学基金(the Natural Science Foundation of Shanghai of China under Grant No.04ZR14034)高等院校博士学科点基金(the China Fund for the Doctoral Program of Higher Education under Grant No.20040251010)
