Name
Abstract | ||
|---|---|---|
In this paper a new approach to information storage and retrieval using cellular neural networks is developed. The objective is achieved by considering a suitable discrete-time model of these networks and by designing them so that the input information are fed via external inputs rather than initial conditions. The technique, which exploits globally asymptotically stable networks, leads to a facilitation of their hardware implementation |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/IJCNN.1999.830750 | IJCNN |
Keywords | Field | DocType |
asymptotic stability,cellular neural nets,discrete time systems,information retrieval,information storage,cellular neural networks,discrete-time model,very large scale integration,cellular neural network,vectors,discrete time,design method,difference equations,feedback,initial condition,design methodology | Data mining,Computer science,Exploit,Information storage,Exponential stability,Artificial intelligence,Cellular neural network,Machine learning,Stability theory | Conference |
Volume | ISSN | ISBN |
6 | 1098-7576 | 0-7803-5529-6 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Grassi, G. | 1 | 0 | 4.06 |
| Acciani, G. | 2 | 0 | 0.34 |