In this paper, we consider the problem of realizing associative memories via cellular neural networks(CNNs). We formulate the synthesis of CNN that can store given binary vectors with improved performance as a constrained optimization problem. Next, we convert the synthesis problem into a generalized eigenvalue problem(GEVP), which can be efficiently solved by recently developed interior point methods. The validity of the proposed approach is illustrated by computer simulations.
|Title of host publication||Proceedings - International Conference on Pattern Recognition|
|Number of pages||4|
|Publication status||Published - 2000|
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Electrical and Electronic Engineering