摘要
研究防止汽车追尾优化识别问题,为解决防汽车追尾快速预报,传统方法在于精确计算安全距离,并未给出具体的控制力度,结合自适应模糊神经网络理论,提出让汽车"缓慢"降速的解决方案,能有效解决防汽车追尾的控制力度问题。采用自适应模糊神经网络模型,设计汽车刹车力度控制规则表,进一步设计防汽车追尾控制器。通过仿真结果可以看出,跟传统的纯模糊控制系统相比,自适应模糊神经网络生成的曲面更加平滑,控制效果更好。并且具有自学习与自适应能力,能够自动生成并调整隶属度函数,提高了快速性和实时性,为汽车防追尾控制器设计提供了参考。
To resolve the problem of automobile＇s rear-end anti-collision,the safety distance is precisely calculated by the traditional method,from which the efforts to control automobile are not given out.In this paper,the solution based on adaptive fuzzy neural network was presented to make the car slow-down,and the problem of automobile＇s rear-end anti-collision was solved effectively.The control rules for the forces of braking car were designed,and the controller for automobiles＇ rear-end anti-collision was designed based on adaptive fuzzy neural network.From the simulation results,compared with the fuzzy control system,the surface produced by the adaptive fuzzy neural network system was smoother,so the result was better.Furthermore,it has the capacity of strong self-learning and self-adapting.And the membership functions can be automatically generated and adjusted,thus its application is more convenient.
出处
《计算机仿真》
CSCD
北大核心
2012年第10期344-347,共4页
Computer Simulation
基金
湖北省科技攻关项目(04AA101C81)
