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A two-level Hamming network for high performance associative memory
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DOI：10.1016/S0893-6080(01)00089-2.png)
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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. (C) 2001 Elsevier Science Ltd. All rights reserved.
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