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外积联想记忆模型的统计性能

Statistical performance of outer-product associative memory models.

作者信息

Gmitro A F, Keller P E, Gindi G R

出版信息

Appl Opt. 1989 May 15;28(10):1940-8. doi: 10.1364/AO.28.001940.

Abstract

A figure of merit, the probability a bit is correct after update, is used to evaluate the performance of randomly coded outer-product associative memory models. Networks with bipolar binary states and nonzero diagonal connections are shown to yield the best performance with respect to this figure of merit. A surprising result is that an all-positive network, one with binary states and positive connections, is superior to a standard Hopfield style network with binary states and bipolar connections. A prescription for the optimal threshold point for the all-positive network is given.

摘要

一个品质因数,即更新后一位正确的概率,被用于评估随机编码的外积联想记忆模型的性能。结果表明,具有双极二进制状态和非零对角连接的网络在此品质因数方面表现最佳。一个令人惊讶的结果是,一个全正网络,即具有二进制状态和正连接的网络,优于具有二进制状态和双极连接的标准霍普菲尔德风格网络。给出了全正网络最优阈值点的规定。

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