This paper presents some properties of stable patterns that can be realized by a certain type of one-dimensional two-layer cellular neural networks (CNNs). We first introduce a notion of admissible local pattern (ALP) set. All the stable patterns of a CNN can be completely determined by the ALP set. We next show that all of 256 possible ALP sets can be realized by two-layer CNNs, while only 59 can be realized by single-layer CNNs. This means two-layer CNNs have a much higher potential for signal processing than single-layer CNNs.
|Journal||Midwest Symposium on Circuits and Systems|
|Publication status||Published - Dec 1 2004|
|Event||The 2004 47th Midwest Symposium on Circuits and Systems - Conference Proceedings - Hiroshima, Japan|
Duration: Jul 25 2004 → Jul 28 2004
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering