2022
DOI: 10.1155/2022/8618586
|View full text |Cite|
|
Sign up to set email alerts
|

An Intelligent Gray Prediction Model Based on Fuzzy Theory

Abstract: In order to improve the forecasting effect of the gray prediction model, this paper combines the fuzzy theory to construct the gray prediction model and explores its forecasting accuracy. Moreover, this paper uses the entropy weight method to obtain the objective weight to correct the subjective weight, which makes the weight calculation more reasonable. In view of the uncertainty of the control signal of the research object, this paper introduces the gray system theory to conduct cluster analysis on the fire … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
3
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2
2

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(3 citation statements)
references
References 20 publications
0
3
0
Order By: Relevance
“…The first order derivative of W t is obtained as w t , which represents the continuous variation of the bus đť‘– revenue rate with time. Let χ be the characteristic function defined in the interval t , t on t , t , w is defined as the following equation ( 11), which represents the continuous variation of the total revenue rate of the bus company with time [10] .…”
Section: Scheduling Schemementioning
confidence: 99%
“…The first order derivative of W t is obtained as w t , which represents the continuous variation of the bus đť‘– revenue rate with time. Let χ be the characteristic function defined in the interval t , t on t , t , w is defined as the following equation ( 11), which represents the continuous variation of the total revenue rate of the bus company with time [10] .…”
Section: Scheduling Schemementioning
confidence: 99%
“…The target position of the next frame in both DR and CR is predicted with the first 10 frames of data. The reaction time of a driver without specialized training is between 0.2 s and 0.3 s [21] . The target position after 0.3 s in RR is predicted with every 9 frames of data.…”
Section: Figure 3 Flowchart Of the Algorithmmentioning
confidence: 99%
“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”
mentioning
confidence: 99%