Intelligent Vehicle Navigation Path Recognition Model Based on Neural Network

DOI

10.2991/meees-18.2018.2How to use a DOI?

Keywords

neural network; intelligent vehicle; path navigation; fuzzy control; recognition model.

Abstract

The traditional vehicle navigation path recognition model based on PID realizes the intelligent vehicle path control based on precise mathematical model, which has low robustness and poor intelligent control performance at high speed. Therefore, the design of intelligent vehicle navigation path recognition model based on neural network, intelligent vehicle kinematics model based on neural network, the design of intelligent vehicle navigation path recognition model based on neural network structure, through the vehicle direction control, to achieve control of the intelligent vehicle navigation path. The multilayer feed forward neural network as the basic structure of T-S fuzzy system simulation, implementation of the regulation by training the weights of neural network times, to complete the design of neural network for intelligent vehicle navigation based on path recognition model. The recognition model is trained to reduce the external interference and improve the control accuracy of the recognition model, to realize the control of the intelligent vehicle path navigation. The experimental results show that the designed intelligent vehicle path navigation recognition model based on neural network has high control accuracy and strong robustness, and the intelligent control effect is good.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Cheng Chen
PY  - 2018/05
DA  - 2018/05
TI  - Intelligent Vehicle Navigation Path Recognition Model Based on Neural Network
BT  - Proceedings of the 2018 International Conference on Mechanical, Electrical, Electronic Engineering & Science (MEEES 2018)
PB  - Atlantis Press
SP  - 6
EP  - 11
SN  - 2352-5401
UR  - https://doi.org/10.2991/meees-18.2018.2
DO  - 10.2991/meees-18.2018.2
ID  - Chen2018/05
ER  -