基于半张量积(STP)的新型关联记忆模型

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-03-19 DOI:10.3389/fncom.2024.1384924
Yanfang Hou, Hui Tian, Chengmao Wang
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引用次数: 0

摘要

一个好的智能学习模型是智能无人系统完整识别场景信息和准确识别特定目标的关键。本研究提出了一种基于矩阵半张量积(STP)的新型关联记忆模型,以解决信息存储容量和关联问题。首先,为了便于建模,介绍了一些前言,并指出了离散霍普菲尔德神经网络(DHNN)应用于联想记忆中的信息存储容量问题。其次,利用 STP 将学习模式等价转换为代数形式。构建了一个记忆矩阵来精确记忆这些学习模式。此外,还开发了一种更新记忆矩阵的算法,以提高模型的联想能力。此外,还提供了另一种算法来展示我们的模型是如何学习和关联的。最后,我们给出了一些例子来证明我们成果的有效性和优势。与主流的 DHNN 相比,我们的模型可以用更少的节点更准确地记忆学习模式。
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A novel associative memory model based on semi-tensor product (STP)
A good intelligent learning model is the key to complete recognition of scene information and accurate recognition of specific targets in intelligent unmanned system. This study proposes a new associative memory model based on the semi-tensor product (STP) of matrices, to address the problems of information storage capacity and association. First, some preliminaries are introduced to facilitate modeling, and the problem of information storage capacity in the application of discrete Hopfield neural network (DHNN) to associative memory is pointed out. Second, learning modes are equivalently converted into their algebraic forms by using STP. A memory matrix is constructed to accurately remember these learning modes. Furthermore, an algorithm for updating the memory matrix is developed to improve the association ability of the model. And another algorithm is provided to show how our model learns and associates. Finally, some examples are given to demonstrate the effectiveness and advantages of our results. Compared with mainstream DHNNs, our model can remember learning modes more accurately with fewer nodes.
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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