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Mendeley readers
Attention Score in Context
| Title |
A General Backpropagation Algorithm for Feedforward Neural Networks Learning
|
|---|---|
| Published in |
IEEE Transactions on Neural Networks and Learning Systems, January 2002
|
| DOI | 10.1109/72.977323 |
| Pubmed ID | |
| Authors |
Xinghuo Yu, M. Onder Efe, Okyay Kaynak |
| Abstract |
A general backpropagation algorithm is proposed for feedforward neural network learning with time varying inputs. The Lyapunov function approach is used to rigorously analyze the convergence of weights, with the use of the algorithm, toward minima of the error function. Sufficient conditions to guarantee the convergence of weights for time varying inputs are derived. It is shown that most commonly used backpropagation learning algorithms are special cases of the developed general algorithm. |
Mendeley readers
The data shown below were compiled from readership statistics for 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | <1% |
| Colombia | 1 | <1% |
| Unknown | 105 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 18 | 17% |
| Student > Bachelor | 15 | 14% |
| Student > Ph. D. Student | 14 | 13% |
| Other | 7 | 7% |
| Professor > Associate Professor | 4 | 4% |
| Other | 13 | 12% |
| Unknown | 36 | 34% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 31 | 29% |
| Computer Science | 20 | 19% |
| Mathematics | 3 | 3% |
| Chemical Engineering | 2 | 2% |
| Agricultural and Biological Sciences | 2 | 2% |
| Other | 9 | 8% |
| Unknown | 40 | 37% |
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 23 September 2014.
All research outputs
#22,760,732
of 25,377,790 outputs
Outputs from IEEE Transactions on Neural Networks and Learning Systems
#1,899
of 3,393 outputs
Outputs of similar age
#128,547
of 130,777 outputs
Outputs of similar age from IEEE Transactions on Neural Networks and Learning Systems
#6
of 6 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,393 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 130,777 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.