Hou Yanfang, Tian Hui, Wang Chengmao
School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China.
School of Automation, Chongqing University of Posts and Telecommunications, Chongqiong, China.
Front Comput Neurosci. 2024 Mar 19;18:1384924. doi: 10.3389/fncom.2024.1384924. eCollection 2024.
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.
一个良好的智能学习模型是智能无人系统中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）的新型联想记忆模型，以解决信息存储容量和联想问题。首先，介绍一些预备知识以方便建模，并指出离散Hopfield神经网络（DHNN）在联想记忆应用中的信息存储容量问题。其次，利用STP将学习模式等效转换为其代数形式。构造一个记忆矩阵以准确记住这些学习模式。此外，开发了一种更新记忆矩阵的算法以提高模型的联想能力。还提供了另一种算法来说明我们的模型如何学习和联想。最后，给出一些例子来证明我们结果的有效性和优势。与主流DHNN相比，我们的模型可以用更少的节点更准确地记住学习模式。
