A two-level hamming network for high performance associative memory.
This paper presents an analysis of a two-level decoupled Hamming network, which is a high performance discrete-time/discrete-state associative memory model. The two-level Hamming memory generalizes the Hamming memory by providing for local Hamming distance computations in the first level and a voting mechanism in the second level. In this paper, we study the effect of system dimension, window size, and noise on the capacity and error correction capability of the two-level Hamming memory. Simulation results are given for both random images and human face images.
Ikeda
N
N
Department of Computer Science & Electronic Engineering, Tokuyama College of Technology, Yamaguchi, Japan. n-ikeda@tokuyama.ac.jp
Watta
P
P
Artiklar
M
M
Hassoun
M H
MH
D000465:Algorithms; D000818:Animals; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D005145:Face; D006801:Humans; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual / Q000502:physiology