模糊神经网络控制的自适应前照灯系统研究

The Study of Adaptive Front System Based on Fuzzy Neural Network

为减少汽车在夜间行驶过程中的视野盲区,并针对传统自适应前照灯控制系统控制精度低,执行动作慢的问题,提出一种基于模糊神经网络算法控制的自适应前照灯控制系统。分析自适应前照灯系统的功能,在Matlab/Simulink中建立自适应前照灯控制系统的数学模型,并引入遗传算法对模糊神经网络进行训练优化,利用所建立的数学模型进行仿真实验测试。实验结果表明,相比于传统的使用模糊控制或神经网络控制的自适应前照灯控制系统,基于模糊神经网络控制的自适应前照灯控制系统控控制精度更高,执行速度更快,鲁棒性更好。

In order to reduce the blind spot in the process of driving at night,and the traditional adaptive headlight control system control precision is low,the problem of slow speed of action execution,is proposed based on adaptive fuzzy neural network control algorithm of the headlight control system. Firstly,the function of the adaptive headlamp system is analyzed,and the mathematical model of the adaptive headlamp control system is established in MATLAB/Simulink according to the data collected by the sensor. Then,the neural network is trained and optimized by genetic algorithm. Finally,the mathematical model is used to test the simulation. The experimental results show that compared with the traditional fuzzy control and adaptive neural network control of the headlight control system based on fuzzy neural network control,adaptive headlight control system control in control precision,speed and robustness,better.