Paper Info
Title
Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network
Abstract
AbstractDue to the continuous progress in the field of vehicle hardware, the condition that a vehicle cannot load a complex algorithm no longer exists. At the same time, with the progress in the field of vehicle hardware, a number of studies have reported exponential growth in the actual operation. To solve the problem for a large number of data transmissions in an actual operation, wireless transmission is proposed for text information (including position information) on the basis of the principles of the maximum entropy probability and the neural network prediction model combined with the optimization of the Huffman encoding algorithm, from the exchange of data to the entire data extraction process. The test results showed that the text-type vehicle information based on a compressed algorithm to optimize the algorithm of data compression and transmission could effectively realize the data compression, achieve a higher compression rate and data transmission integrity, and after decompression guarantee no distortion. Therefore, it is important to improve the efficiency of vehicle information transmission, to ensure the integrity of information, to realize the vehicle monitoring and control, and to grasp the traffic situation in real time.
Year
DOI
Venue
2018
10.1155/2018/1067927
Periodicals
Field
DocType
Volume
Data compression ratio,GRASP,Data transmission,Control theory,Real-time computing,Huffman coding,Data extraction,Principle of maximum entropy,Artificial neural network,Data compression,Mathematics
Journal
2018
Issue
ISSN
Citations 
1
1076-2787
0
PageRank 
References 
Authors
0.34
15
9
Name
Order
Citations
PageRank
Jingfeng Yang1618.34
Nanfeng Zhang200.34
Ming Li3159.78
Yanwei Zheng4507.62
Li Wang505.07
Yong Li620.73
Ji Yang795.93
Yifei Xiang800.34
Lufeng Luo972.62