Reconfigurable hardware for neural networks: binary versus stochastic
β Scribed by Nadia Nedjah; Luiza de Macedo Mourelle
- Publisher
- Springer-Verlag
- Year
- 2007
- Tongue
- English
- Weight
- 325 KB
- Volume
- 16
- Category
- Article
- ISSN
- 0941-0643
No coin nor oath required. For personal study only.
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## Abstract Stable cellular neural networks with binary outputs implement a nonβlinear mapping between sets of input and output images. Such a mapping is studied in detail. We prove two theorems: the first one yields a sufficient condition in order that the nonβlinear mapping be wellβdefined; the s
In order to be able to take full advantage of the great application potential that lies in cellular neural networks (CNNs) we need to have successful design and learning techniques as well. In almost any analogic CNN algorithm that performs an image processing task, binary CNNs play an important rol