Binary Associative Memories with Complemented Operations

IF 1.6 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS
Arturo Gamino-Carranza
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引用次数: 0

Abstract

Abstract Associative memories based on lattice algebra are of great interest in pattern recognition applications due to their excellent storage and recall properties. In this paper, a class of binary associative memory derived from lattice memories is presented, which is based on the definition of new complemented binary operations and threshold unary operations. The new learning method generates memories M and W; the former is robust to additive noise and the latter is robust to subtractive noise. In the recall step, the memories converge in a single step and use the same operation as the learning method. The storage capacity is unlimited, and in autoassociative mode there is perfect recall for the training set. Simulation results suggest that the proposed memories have better performance compared to other models.
具有互补运算的二进制联想存储器
基于格代数的联想记忆以其优异的存储和回忆性能在模式识别领域受到广泛关注。本文提出了一类由点阵存储器衍生而来的二元联想存储器,它是基于新的互补二进制运算和阈值一元运算的定义。新的学习方法产生记忆M和W;前者对加性噪声具有鲁棒性,后者对减性噪声具有鲁棒性。在回忆步骤中,记忆在一个步骤中收敛,使用与学习方法相同的操作。存储容量是无限的,并且在自关联模式下对训练集有完美的召回。仿真结果表明,与其他模型相比,所提出的存储器具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.10
自引率
21.10%
发文量
0
审稿时长
4.2 months
期刊介绍: The International Journal of Applied Mathematics and Computer Science is a quarterly published in Poland since 1991 by the University of Zielona Góra in partnership with De Gruyter Poland (Sciendo) and Lubuskie Scientific Society, under the auspices of the Committee on Automatic Control and Robotics of the Polish Academy of Sciences. The journal strives to meet the demand for the presentation of interdisciplinary research in various fields related to control theory, applied mathematics, scientific computing and computer science. In particular, it publishes high quality original research results in the following areas: -modern control theory and practice- artificial intelligence methods and their applications- applied mathematics and mathematical optimisation techniques- mathematical methods in engineering, computer science, and biology.
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