基于神经网络的智能车辆导航路径识别模型Neural network based navigation path recognition model of intelligent vehicle
陈诚
摘要(Abstract)：
传统基于PID的车辆导航路径识别模型,基于精准的数学模型实现智能车辆路径控制,在高速情况下具有较低的鲁棒性,智能控制性能差。因此,基于智能车辆运动学模型,设计基于神经网络智能车辆导航路径识别模型结构,通过神经网络对车辆行驶方向进行控制,实现对智能车辆路径导航的控制。将多层前馈型神经网络作为基础结构对T-S模糊系统进行模拟,通过多次训练对神经网络的权值实施调控,完成基于神经网络智能车辆导航路径识别模型的设计。对识别模型实施训练,降低外界的干扰,提高识别模型的控制精度,实现对智能车辆路径导航的控制。实验结果说明,设计的基于神经网络的智能车辆路径导航识别模型控制精度高且鲁棒性较强,智能控制效果佳。
关键词(KeyWords)： 神经网络;智能车辆;路径导航;模糊控制;识别模型;控制精度;鲁棒性
基金项目(Foundation):
作者(Author): 陈诚
DOI: 10.16652/j.issn.1004-373x.2018.11.028
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