Fuzzy neural network based traffic prediction and congestion control in high-speed networks

2000 ◽  
Vol 15 (2) ◽  
pp. 144-149 ◽  
Author(s):  
Xiang Fei ◽  
Xiaoyan He ◽  
Junzhou Luo ◽  
Jieyi Wu ◽  
Guanqun Gu
Keyword(s):  
Neural Network ◽  
Congestion Control ◽  
High Speed ◽  
Fuzzy Neural Network ◽  
Traffic Prediction ◽  
High Speed Networks ◽  
Fuzzy Neural

scholarly journals Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network

2021 ◽  
Vol 12 (1) ◽  
pp. 597
Author(s):  
Ahmed Thamer Radhi ◽  
Wael Hussein Zayer
Keyword(s):  
Neural Network ◽  
Equivalent Circuit ◽  
High Speed ◽  
Fuzzy Neural Network ◽  
Induction Motors ◽  
Stator Winding ◽  
Three Phase ◽  
Fuzzy Neural ◽  
Diagnosis Method ◽  
Faults Diagnosis

The paper deals with faults diagnosis method proposed to detect the inter-turn and turn to earth short circuit in stator winding of three-phase high-speed solid rotor induction motors. This method based on negative sequence current of motor and fuzzy neural network algorithm. On the basis of analysis of 2-D electromagnet field in the solid rotor the rotor impedance has been derived to develop the solid rotor induction motor equivalent circuit. The motor equivalent circuit is simulated by MATLAB software to study and record the data for training and testing the proposed diagnosis method. The numerical results of proposed approach are evaluated using simulation of a three-phase high-speed solid-rotor induction motor of two-pole, 140 Hz. The results of simulation shows that the proposed diagnosis method is fast and efficient for detecting inter-turn and turn to earth faults in stator winding of high-speed solid-rotor induction motors with different faults conditions

An Intelligent Cruise Controller for High-Speed Train Operation Based on Fuzzy Neural Network Theory

2013 ◽  
Vol 300-301 ◽  
pp. 1405-1411 ◽  
Author(s):  
Long Sheng Wang ◽  
Hong Ze Xu ◽  
Heng Yu Luo
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Fuzzy Neural Network ◽  
Single Point ◽  
Network Control ◽  
High Speed Train ◽  
Mass Model ◽  
Fuzzy Neural ◽  
Train Operation ◽  
Automatic Train Operation

An intelligent control strategy is proposed in this paper, which is applied to the high-speed train ATO (Automatic Train Operation) system in the cruise condition. The dynamics of a high-speed train is discussed based on a typical single-point-mass model and the force analysis in cruise state is studied. A fuzzy neural network control algorithm is incorporated into the ATO system aiming at improving the velocity and position tracking performance in the cruise operation of high-speed train. This control scheme adjusts the parameters of membership functions on-line and does not rely on the precise system parameters such as resistance coefficients which are very difficult to measure in practice. The numerical simulation verifies the effectiveness of this fuzzy neural network algorithm.

scholarly journals Intelligent control of horizontal vibration of high-speed elevator based on gas–liquid active guide shoes

2021 ◽  
Vol 22 ◽  
pp. 2
Author(s):  
Qin He ◽  
Peng Zhang ◽  
Shunxin Cao ◽  
Ruijun Zhang ◽  
Qing Zhang
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Mean Value ◽  
Network Control ◽  
Neural Network Control ◽  
Horizontal Vibration ◽  
Suppression Effect ◽  
Fuzzy Neural

Aiming at the inconsistency between the vibration of the car and the car frame in the actual operation of a high-speed elevator and the horizontal vibration caused by the roughness excitation of the guide rail, this study designs a gas–liquid active guide shoe and establishes a horizontal vibration model of the 8-DOF high-speed elevator car system separated from the car and the car frame. Then, the correctness of the model is verified by experiments. Based on this, a fuzzy neural network intelligent vibration reduction controller based on the Mamdani model is designed and simulated by MATLAB. The results show that the root mean square value, mean value, and maximum value of vibration acceleration are reduced by more than 55% after using the fuzzy neural network control method, and the suppression effect is better than that of BP neural network control. Therefore, the intelligent vibration absorption controller designed by this research institute can effectively suppress the horizontal vibration of high-speed elevators.

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