Filtering and unfolding using neural networks
✍ Scribed by Thomas Lindblad; Géza Székely
- Publisher
- Elsevier Science
- Year
- 1993
- Tongue
- English
- Weight
- 286 KB
- Volume
- 328
- Category
- Article
- ISSN
- 0168-9002
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
## Abstract A new design technique using the FDTD method and neural networks is applied to a microstrip filter. The total design time is reduced by two means. First, an iterative ARMA signal estimation technique is utilized to reduce the computation time for each FDTD run. Second, the number of FDT
This paper proposes a novel realization ~[' nonlinear .filters suitable ./br the edgepreservhl 9 smoothhl# q/'an hnage degraded by a mixed noise environment composed o/the Gaussian and impulsive noises. This.filter consists qf a layered neural network and a median filter. By using layered neural net
When a nonstationary signal containing sudden changes, such as an image signal, is degraded by an additive noise, a powerful means to recover the signal is to use a nonlinear filter. This paper uses a layered neural network, and proposes a new method to construct a nonlinear filter for restoring a s