麦弗逊悬架在ADMS中嵌入遗传因子的优化研究

基于麦弗逊汽车悬架动力神经结构中的麦弗逊汽车悬架动力神经结构运动参数与数据优化瓶颈，利用基于麦弗逊汽车悬架动力神经结构空间结构和麦弗逊汽车悬架动力神经结构运动性能建立麦弗逊汽车悬架动力神经结构运动模型。同时利用数学运算数学运算几何定位节点的关联函数。同时利用汽车遗传因子方法进一步优化麦弗逊汽车悬架动力神经结构运动模型，并嵌入在Adams中。仿真实验表明：在Adams中嵌入汽车遗传因子后可有效控制定位节点的滑移量。所设计的ADAMS嵌入汽车遗传因子的优化设计方法，能够在汽车汽车悬架动力参数与数据优化时快速实现，证明了所建立模型的正确性和优化设计方法的有效性。

The Research on loptimization of Macpherson Suspension Embedded Genetic Factor by ADAMS

Mcpherson suspension dynamic neural structures in the Mcpherson suspension dynamic neural structure parameters and data based on loptimization bottleneck, the Mcpherson suspension dynamic neural structure movement model built the Mcpherson suspension dynamic neural structure spatial structure and Mcpherson suspension dynamic neural structure based on exercise performance. At the same time correlation function arithmetic arithmetic geometric positioning node. At the same time by using the factor method of automobile genetic loptimize Mcpherson suspension dynamic neural structure motion model, and is embedded in the Adams. Simulation results show that：slip into the genetic factor in Adams can effectively control the positioning of nodes.loptimization design method and the design of ADAMS into the genetic factor, can quickly realize the car suspension dynamic parameters and data loptimization, proved the validity and correctness of the loptimization design method of the model.