Name
Title | ||
|---|---|---|
Piecewise-exponential approximation for fast time-domain simulation of 2-D cellular neural networks |
Abstract | ||
|---|---|---|
Cellular neural networks (CNNs) were introduced as promising image processing systems. However, since analytical design techniques are rarely available, extensive simulation is the main practical tool for developing significant applications. This paper presents a new algorithm for fast simulation of large-scale CNNs. It is based on the discretization of the sigmoid generating the output from the state of each cell. This discretization leads to a piecewise exponential approximation of the time-domain solution. Computation is only required when the output of a cell jumps to a different discrete level and involves only this cell and its neighbors. The algorithm is spatially adaptive since the computational effort is concentrated on the most rapidly evolving portions of the array. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/TCSII.2004.832768 | IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing |
Keywords | Field | DocType |
cellular neural nets,exponential distribution,piecewise linear techniques,time-domain analysis,2d cellular neural networks,adaptive computation,fast time-domain simulation,image processing systems,piecewise-exponential approximation,cnns,cellular neural networks,fast simulation of large arrays,piecewise exponential approximation | Time domain,Discretization,Mathematical optimization,Exponential function,Control theory,Computer science,Image processing,Algorithm,Cellular neural network,Piecewise,Sigmoid function,Computation | Journal |
Volume | Issue | ISSN |
51 | 8 | 1549-7747 |
Citations | PageRank | References |
1 | 0.45 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| De Sandre, G. | 1 | 35 | 3.46 |
| Premoli, Amedeo | 2 | 25 | 2.31 |