Name
Abstract | ||
|---|---|---|
Recent papers in the literature introduced a class of neural networks (NNs) with memristors, named dynamic-memristor (DM) NNs, such that the analog processing takes place in the charge-flux domain, instead of the typical current-voltage domain as it happens for Hopfield NNs and standard cellular NNs. One key advantage is that, when a steady state is reached, all currents, voltages, and power of a ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2017.2688404 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Memristors,Artificial neural networks,Neurons,Capacitors,Standards,Biological neural networks,Convergence | Lyapunov function,Memristor,Physical neural network,Computer science,Exponential stability,Types of artificial neural networks,Artificial intelligence,Multistability,Cellular neural network,Machine learning,Stability theory | Journal |
Volume | Issue | ISSN |
29 | 5 | 2162-237X |
Citations | PageRank | References |
4 | 0.38 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mauro Di Marco | 1 | 205 | 18.38 |
| Mauro Forti | 2 | 398 | 36.80 |
| Luca Pancioni | 3 | 207 | 17.58 |