Research of Optimizing Ignition Control System in Gaseous Fuel Engine Based on RBF Neural Network

2008 ◽  
Author(s):  
Hongwei Cui
Keyword(s):  
Neural Network ◽  
Control System ◽  
Rbf Neural Network ◽  
Gaseous Fuel

scholarly journals Fuzzy Neural Network Q-Learning Method for Model Disturbance Change: A Deployable Antenna Panel Application

2019 ◽  
Vol 2019 ◽  
pp. 1-21
Author(s):  
Zhiyong Liu ◽  
Hong Bao ◽  
Song Xue ◽  
Jingli Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Basis Function ◽  
Fuzzy Neural Network ◽  
Rbf Neural Network ◽  
Learning Method ◽  
Active Deformation ◽  
Q Learning ◽  
Adjustment Mechanism ◽  
Fuzzy Neural

This paper addresses the disturbance change control problem with an active deformation adjustment mechanism on a 5-meter deployable antenna panel. A fuzzy neural network Q-learning control (FNNQL) strategy is proposed in this paper for the disturbance change to improve the accuracy of the antenna panel. In the proposed method, the error of the model disturbance is reduced by introducing the fuzzy radial basis function (RBF) neural network into Q-learning, and the parameters of the fuzzy RBF neural network were optimized and adjusted by a Q-learning method. This allows the FNNQL controller to have a strong adaptability to deal with the disturbance change. Finally, the proposed method has been adopted in the middle plate of a 5-meter deployable antenna panel, and it was found that the method could successfully adapt the model disturbance change in the antenna panel. Results of the simulation also show that the whole control system meets the required accuracy requirements.

Research on Electric Vehicle Road Identification Method Based on RBF Neural Network

2014 ◽  
Vol 543-547 ◽  
pp. 1413-1416
Author(s):  
Zhi Yu Huang ◽  
Jia Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Simulation Model ◽  
Rbf Neural Network ◽  
Relevant Information ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
The Road ◽  
Road Condition ◽  
Motor Control System

Accurately identifying road condition can send relevant information to the motor control system, so that control system of the motor can adjust the control strategy timely, eventually, the intelligent and optimal control of electric vehicles is realized. In this paper, according to these mathematical model, the permanent magnet synchronous motors simulation model and vehicles simulation model are proposed. Then, output torque of motor and speed of motor are served as the input of RBF neural network, which helps road condition to be identified. The simulation result shows that the road condition is well identified by proposed method based on RBF neural network.

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