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前馈神经网络在汽车悬架设计中的应用


□ 任 远 白广忱

  摘 要:提出采用GA-BP贝叶斯算法来建立悬架运动学分析近似模型。该算法是一种新型前馈神经网络训练算法,它以提高网络的泛化性能为主旨,其训练目标在于获取对应于后验分布最大值的权值向量。以双横臂式前独立悬架为例,采用GA-BP贝叶斯算法建立了以车轮接地点侧向最大滑移量为输出的运动学分析近似模型,并与L-MBP算法、多项式回归和广义回归神经网络这三种方法进行了比较。结果表明,基于GA-BP贝叶斯算法的近似模型的预测精度明显高于其他几种模型,并且受随机因素的影响很小。
  关键词:神经网络; 遗传算法; 近似模型; 悬架
  中图分类号:TP183; TP391.72文献标志码:A
  文章编号:1001-3695(2009)06-2273-03
  doi:10.3969/j.issn.1001-3695.2009.06.083
  
  Application of feedforward NN in design of vehicle suspension
  REN Yuan, BAI Guang-chen
  (School of Jet Propulsion, Beijing University of Aeronautics & Astronautics, Beijing 100083, China)
  Abstract:
  The GA-BP Bayesian algorithm was used to establish the kinematics analysis approximation model for suspension. This algorithm is a new feedforward NN training algorithm developed by the authors. Its aim is to improve the generalization ability of networks, and it trains a network with the purpose of obtaining the weights corresponding with the maximum posterior probability. Taking a wishbone type independent front suspension for example, the GA-BP Bayesian algorithm was used to establish the kinematics analysis approximation model whose output was the maximum sideways displacement, and the comparison with three other methods was made. They were L-M backpropagation, polynomial regression, and general regression neural network. The results show that the approximation models based on GA-BP Bayesian algorithm have higher prediction accuracy than the ones based on three other methods, and that the prediction accuracy they have can hardly be affected by random factors. ......
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