title: A synthesis procedure for associative memories based on space-varying cellular neural networks.
abstract: 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.
authors.affiliation: Department of Control and Instrumentation Engineering, Korea University, Chochiwon, Chungnam, South Korea. jpark@tiger.korea.ac.kr
authors.forename: J
authors.initials: J
authors.lastname: Park
authors.forename: H Y
authors.initials: HY
authors.lastname: Kim
authors.forename: Y
authors.initials: Y
authors.lastname: Park
authors.forename: S W
authors.initials: SW
authors.lastname: Lee
mesh_terms: D008568:Memory / Q000502:physiology*; D008962:Models, Theoretical; D016571:Neural Networks, Computer