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Fault compensation by online updating of genetic algorithm-selected neural network model for model predictive control

Overview of attention for article published in SN Applied Sciences, October 2019
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 X users

Citations

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11 Dimensions

Readers on

mendeley
10 Mendeley
Title
Fault compensation by online updating of genetic algorithm-selected neural network model for model predictive control
Published in
SN Applied Sciences, October 2019
DOI 10.1007/s42452-019-1526-9
Authors

Seong Hyeon Hong, Jackson Cornelius, Yi Wang, Kapil Pant

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 30%
Student > Doctoral Student 2 20%
Student > Ph. D. Student 2 20%
Unspecified 1 10%
Unknown 2 20%
Readers by discipline Count As %
Computer Science 4 40%
Engineering 2 20%
Environmental Science 1 10%
Unspecified 1 10%
Unknown 2 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 November 2019.
All research outputs
#15,587,562
of 23,173,635 outputs
Outputs from SN Applied Sciences
#475
of 1,369 outputs
Outputs of similar age
#223,767
of 361,462 outputs
Outputs of similar age from SN Applied Sciences
#57
of 139 outputs
Altmetric has tracked 23,173,635 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,369 research outputs from this source. They receive a mean Attention Score of 3.1. This one has gotten more attention than average, scoring higher than 51% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 361,462 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.