Study on soft computing arithmetic for vehicle yaw rate based on ANFIS Online publication date: Fri, 10-Apr-2015
by Jianwei Wei; Minxiang Wei; Wanzhong Zhao
International Journal of Vehicle Design (IJVD), Vol. 56, No. 1/2/3/4, 2011
Abstract: A soft computing arithmetic based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate vehicle yaw rate in a driver-vehicle closed loop system, and vehicle yaw rate is considered as a nonlinear mapping of time series of rack displacement and lateral acceleration. Simulation study on soft computing of vehicle yaw rate in the driver-vehicle closed loop system is conducted, and the performance of the soft computing arithmetic based on ANFIS is evaluated with the help of actual vehicle test data. Results indicate that the generalisation of the soft computing arithmetic based on ANFIS is better than that based on Radial Basis Function (RBF) neural network.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Vehicle Design (IJVD):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com