Modeling and Analysis of Associative Memory Networks (EIA)

联想记忆网络 (EIA) 的建模和分析

• 批准号: 8710868
• 负责人: Anthony Kuh
• 金额: $ 7.76万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-07-01 至 1989-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8710868&HistoricalAwards=false
• 关键词: Modeling; Analysis; Associative; Memory; Networks

Associative memories are composed of a collection of interconnected elements having data storage capabilites. The elements are accessed in parallel by the content of a data probe vector rather than by a specific address. Recently, models referred to as the Hopfield AMN have received much attention. These models are simple to analyze and exhibit some of the behavioral features of neural networks. To incorporate these features, some new models will be explored which retain enough of the simple structure of the Hopfield AMN that makes them amenable to analysis. The objective is to obtain a better understanding of the properties and behavior of various types of AMN. The principal investigator is analyzing the dynamic and information storage capabilities of several new models. Many of the new models are subject to some physical constraints and interconnection costs, and it is appropriate to formulate various network design and analysis problems as optimization problems. The research will have important implications for the use of AMN in computer architectures of the future, matched to challenging problems in signal processing, communications, control, and artificial intelligence. Understanding the relationship between parameters of various AMN and their information storage capacities is a step towards determining the most useful aspects of this form of parallel distributed computing.

联想记忆是由一系列相互关联的 具有数据存储能力的元件。 这些元素是在 通过数据探测向量的内容而不是通过特定的 地址. 最近，被称为Hopfield AMN的模型已经收到了 多注意。 这些模型很容易分析和展示一些 神经网络的行为特征。 将这些 功能，将探索一些新的模式，保留足够的 Hopfield AMN的简单结构，使他们能够适应 分析. 目的是为了更好地了解 各种类型AMN的性质和行为。 校长 调查人员正在分析动态和信息存储 几种新模式的能力。 许多新型号都受到 一些物理限制和互连成本，它是 适用于制定各种网络设计和分析问题， 优化问题 该研究将对AMN在以下方面的应用具有重要意义： 未来的计算机体系结构，与挑战性的问题相匹配， 信号处理、通信、控制和人工智能。 了解各种AMN参数之间的关系， 它们的信息存储能力是确定 这种形式的并行分布式计算的最有用的方面。