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X Demographics
Mendeley readers
Attention Score in Context
| Title |
An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking
|
|---|---|
| Published in |
Frontiers in Neurorobotics, September 2017
|
| DOI | 10.3389/fnbot.2017.00045 |
| Pubmed ID | |
| Authors |
Lei Ding, Lin Xiao, Bolin Liao, Rongbo Lu, Hua Peng |
| Abstract |
To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new neural network for complex-valued systems of linear equation in complex domain is proposed and theoretically proved to be convergent within finite time. Then, the illustrative results show that the new neural network model has the higher precision and the higher convergence rate, as compared with the gradient neural network (GNN) model and the ZNN model. Finally, the application for controlling the robot using the proposed method for the complex-valued systems of linear equation is realized, and the simulation results verify the effectiveness and superiorness of the new neural network for the complex-valued systems of linear equation. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 50% |
| Switzerland | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 27% |
| Student > Master | 2 | 18% |
| Professor | 2 | 18% |
| Student > Doctoral Student | 1 | 9% |
| Lecturer | 1 | 9% |
| Other | 0 | 0% |
| Unknown | 2 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 4 | 36% |
| Computer Science | 3 | 27% |
| Business, Management and Accounting | 2 | 18% |
| Design | 1 | 9% |
| Unknown | 1 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 09 October 2017.
All research outputs
#14,362,315
of 22,999,744 outputs
Outputs from Frontiers in Neurorobotics
#347
of 876 outputs
Outputs of similar age
#175,613
of 316,305 outputs
Outputs of similar age from Frontiers in Neurorobotics
#8
of 19 outputs
Altmetric has tracked 22,999,744 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 876 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 56% 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 316,305 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.