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昆明冶金高等专科学校学报 ›› 2025, Vol. 41 ›› Issue (05): 64-.DOI: 10.3969/j.issn.1009-0479.2025.05.011

• 电子信息技术 • 上一篇    下一篇

Matlab构建神经网络二值化的学习框架分析

  

  1. 昆明冶金高等专科学校招生就业中心 云南 昆明 65003
  • 出版日期:2025-10-01 发布日期:2026-03-25
  • 作者简介:王艳玲 (1984-),女,新疆沙湾人,副教授,理学学士,主要从事计算机科学与技术方面的研究。

Analysis of the Learning Framework for ConstructingBinary Neural Network Based on Matlab

  1. Enrollment & Employment Center, Kunming Metallurgy College, Kunming 650033, China
  • Online:2025-10-01 Published:2026-03-25

摘要: 二值化神经网络是为了适应资源限制环境而创造的一种神经网络的特殊形式,主要特征在于网络权重 和激活函数的输出值在该形式下都会被二值化,即全部转化为 0或 1的二进制形式。二值化神经网络的应用可 以减少模型对存储空间的占用,以及对计算资源的消耗,因此被广泛应用于各种移动设备以及嵌入式系统中。 出于提升二值化网络计算效率、降低其能耗的目的,将围绕神经网络二值化的主要概念及框架构建要点进行分 析,并提出用 Matlab构建二值化神经网络学习框架的方法,从而为二值化神经网络更好地利用有限资源,并在 各种环境中部署和运行深度学习模型提供学术参考和技术支持。

关键词: Matlab, 二值化神经网络, 学习框架, 网络参数, 训练函数

Abstract: Binary neural network (BNN) is a specialized form of neural networks designed to adapt to re-source-constrained environments. I's core characteristic is that both the network weights and activationfunction outputs are binarized, i. e. , converted entirely to the binary form of O or 1. BNN can reducethe model's storage space occupation and computing resource consumption, thus being widely applied invarious mobile devices and embedded systems. To enhance the computational efficiency of BNN and re-duce their energy consumption, this paper analyzes the core concepts of neural network binarization andkey points of framework construction, and proposes a method for building a BNN learning framework u-sing Matlab. This provides academic references and technical support for BNN to belter utilize limited re-sources and for deploying and running deep learning models in various environments

Key words: Matlab, Binarytraining functions Neural Network(BNN), learning framework, network parameters, training functions