...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>SAE International Journal of Passenger Cars - Mechanical Systems >Prediction of Automotive Ride Performance Using Adaptive Neuro-Fuzzy Inference System and Fuzzy Clustering
【24h】

Prediction of Automotive Ride Performance Using Adaptive Neuro-Fuzzy Inference System and Fuzzy Clustering

机译：基于自适应神经模糊推理系统和模糊聚类的汽车行驶性能预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Artificial intelligence systems are highly accepted as a technology to offer an alternative way to tackle complex and non-linear problems. They can learn from data, and they are able to handle noisy and incomplete data. Once trained, they can perform prediction and generalization at high speed. The aim of the present study is to propose a novel approach utilizing the adaptive neuro-fuzzy inference system (ANFIS) and the fuzzy clustering method for automotive ride performance estimation. This study investigated the relationship between the automotive ride performance and relative parameters including speed, spring stiffness, damper coefficients, ratios of sprung and unsprung mass. A Takagi-Sugeno fuzzy inference system associated with artificial neuro network was employed. The C-mean fuzzy clustering method was used for grouping the data and identifying membership functions. The prediction results were compared with simulation testing data and experimental data of a typical A-Class automobile.
机译：人工智能系统作为一种提供解决复杂和非线性问题的替代方法的技术而受到高度认可。他们可以从数据中学习，并且能够处理嘈杂和不完整的数据。一旦受过训练，他们就可以高速执行预测和概括。本研究的目的是提出一种利用自适应神经模糊推理系统（ANFIS）和模糊聚类方法进行汽车行驶性能估计的新方法。这项研究调查了汽车行驶性能与相关参数之间的关系，这些相关参数包括速度，弹簧刚度，阻尼系数，簧载质量与非簧载质量的比率。使用了与人工神经网络相关的Takagi-Sugeno模糊推理系统。 C均值模糊聚类方法用于数据分组和识别隶属度函数。将预测结果与典型A级汽车的模拟测试数据和实验数据进行了比较。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号