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

Event
SAE 2015 Noise and Vibration Conference and Exhibition
Authors Abstract
Content
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.
Meta TagsDetails
DOI
https://doi.org/10.4271/2015-01-2260
Pages
12
Citation
Shi, T., Chen, S., and Wang, D., "Prediction of Automotive Ride Performance Using Adaptive Neuro-Fuzzy Inference System and Fuzzy Clustering," SAE Int. J. Passeng. Cars - Mech. Syst. 8(3):916-927, 2015, https://doi.org/10.4271/2015-01-2260.
Additional Details
Publisher
Published
Jun 15, 2015
Product Code
2015-01-2260
Content Type
Journal Article
Language
English