Collision avoidance algorithm based on ANFIS
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摘要: 应用自适应神经模糊推理系统(ANFIS)建立了车辆避撞算法模型,为加快收敛速度,运用线性递归最小二乘法(RLSE)和最陡下降法(SD)组成的混合算法通过前向和后向通道分别进行了模型的前提参数和结论参数的辨识.为取得模型训练所需的数据对,设计了两车跟随的实验,试验中尝试性的采用GPS来获取两车的车间距和车速差,经过模型训练和仿真输出,该方法能够模仿驾驶员的操作,并且操作更为平滑.这种方法改变了以往采用经验来确定隶属函数的缺陷,使模型的建立更为可靠.
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关键词:
- 仿真 /
- 算法 /
- 避撞 /
- 自适应神经模糊推理系统
Abstract: A vehicle collision avoidance model was established according to adaptive neuro-fuzzy inference system. In order to improve rapidity of convergence, a hybrid algorithm was proposed. For some linear parameters such as consequent parameters, recursive least square algorithm was used to update it. For other nonlinear parameters such as premise parameters, steepest descent method was used to identify it. To get the teaching data to train the model, an experiment was designed. Global position system(GPS) module was adopted in the experiment to get the headway and velocity difference between lead vehicle and following vehicle. Based on the training data obtained by experiment, the model was trained and a control output was simulated. By comparing the simulation result and the experiment data, it shows that the model can simulate manipulation of driver accurately and even more smoothly.-
Key words:
- simulation /
- algorithm /
- collision avoidance /
- adaptive neuro-fuzzy inference system
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