一种联想记忆的加权投票模型。
A weighted voting model of associative memory.
作者信息
Mu Xiaoyan, Watta Paul, Hassoun Mohamad H
机构信息
Department of Electrical and Computer Engineering, Rose-Hulman Institute of Technology, Terre Haute, IN 47803, USA.
出版信息
IEEE Trans Neural Netw. 2007 May;18(3):756-77. doi: 10.1109/TNN.2007.891196.
This paper presents an analysis of a random access memory (RAM)-based associative memory which uses a weighted voting scheme for information retrieval. This weighted voting memory can operate in heteroassociative or autoassociative mode, can store both real-valued and binary-valued patterns and, unlike memory models, is equipped with a rejection mechanism. A theoretical analysis of the performance of the weighted voting memory is given for the case of binary and random memory sets. Performance measures are derived as a function of the model parameters: pattern size, window size, and number of patterns in the memory set. It is shown that the weighted voting model has large capacity and error correction. The results show that the weighted voting model can successfully achieve high-detection and -identification rates and, simultaneously, low-false-acceptance rates.
本文介绍了一种基于随机存取存储器（RAM）的联想存储器分析，该存储器使用加权投票方案进行信息检索。这种加权投票存储器可以在异联想或自联想模式下运行，可以存储实值和二进制值模式，并且与其他存储器模型不同，它配备了一种拒绝机制。针对二进制和随机存储器集的情况，给出了加权投票存储器性能的理论分析。性能指标是作为模型参数的函数推导出来的：模式大小、窗口大小和存储器集中的模式数量。结果表明，加权投票模型具有大容量和纠错能力。结果表明，加权投票模型可以成功实现高检测率和识别率，同时实现低误接受率。
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