Name
Title | ||
|---|---|---|
Robustly Fitting and Forecasting Dynamical Data With Electromagnetically Coupled Artificial Neural Network: A Data Compression Method. |
Abstract | ||
|---|---|---|
In this paper, a dynamical recurrent artificial neural network (ANN) is proposed and studied. Inspired from a recent research in neuroscience, we introduced nonsynaptic coupling to form a dynamical component of the network. We mathematically proved that, with adequate neurons provided, this dynamical ANN model is capable of approximating any continuous dynamic system with an arbitrarily small erro... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TNNLS.2015.2508931 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Neurons,Artificial neural networks,Biological system modeling,Mathematical model,Robustness,Electromagnetic coupling,Brain modeling | Coupling,Jacobian matrix and determinant,Computer science,Harmonic,Robustness (computer science),Dynamic data,Artificial intelligence,Artificial neural network,Data compression,Machine learning,Perturbation (astronomy) | Journal |
Volume | Issue | ISSN |
28 | 6 | 2162-237X |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziyin Wang | 1 | 7 | 3.96 |
| Mandan Liu | 2 | 4 | 3.44 |
| Yi-Cheng Cheng | 3 | 54 | 7.15 |
| Rubin Wang | 4 | 141 | 25.54 |