A synthesis procedure for associative memories based on space-varying cellular neural networks.
In this paper, we consider the problem of realizing associative memories via space-varying CNNs (cellular neural networks). Based on some known results and a newly derived theorem for the CNN model, we propose a synthesis procedure for obtaining a space-varying CNN that can store given bipolar vectors with certain desirable properties. The major part of our synthesis procedure consists of solving generalized eigenvalue problems and/or linear matrix inequality problems, which can be efficiently solved by recently developed interior point methods. The validity of the proposed approach is illustrated by a design example.
Park
J
J
Department of Control and Instrumentation Engineering, Korea University, Chochiwon, Chungnam, South Korea. jpark@tiger.korea.ac.kr
Kim
H Y
HY
Park
Y
Y
Lee
S W
SW
D008568:Memory / Q000502:physiology*; D008962:Models, Theoretical; D016571:Neural Networks, Computer