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基于memristor的RDBO-CNN电路设计和图像多分类识别的应用
Gaoyong Han1,2, Guanxiang Cheng1,2, Yanfeng Wang1,2
1College of Electronic and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002 China.
Cognitive neurodynamics
|August 21, 2025
概括
这项研究引入了一种用于增强虫优化 (RDBO) 和卷积神经网络 (CNN) 的新型记忆器电路. 该RDBO-CNN电路有效地优化图像分类任务的CNN参数.
科学领域:
- 人工智能算法的硬件实现
- 神经形态计算
- 优化算法
背景情况:
- 传统的卷积神经网络 (CNN) 需要广泛的超参数调整,并且在硬件中不易实现.
- 现有的虫优化 (DBO) 算法面临着勘探-开发平衡和局部优化的挑战,阻碍了它们在CNN参数优化中的应用.
研究的目的:
- 提出一种新的memristor交叉杆架构电路,用于实现与卷积神经网络 (CNN) 集成的强化虫优化 (RDBO) 算法.
- 增强DBO算法以克服局部最佳和不平衡的勘探/开发等限制,以实现高效的CNN参数优化.
- 验证拟议的RDBO-CNN电路用于图像识别和分类任务的性能.
主要方法:
- 开发一个memristor横杆架构电路,包括养,存储,滚球,跳舞,子群和CNN模块.
- 实施强化虫优化 (RDBO) 算法,结合巨型虫和螺旋搜索策略.
- 将RDBO算法与CNN模块 (卷积,聚合和完全连接的层) 集成用于图像分类.
主要成果:
- 在MNIST数据集上的图像分类任务中,RDBO-CNN电路证明了可行性和准确性.
- 通过对卫星图像识别数据集 (RSI-CB) 的进一步验证,证实了该电路的有效性和准确性.
- 拟议的硬件实现为优化和部署CNN提供了可行的解决方案.
结论:
- 开发的基于memristor的RDBO-CNN电路为神经网络优化和图像分类提供了高效的硬件实现.
- 改进的RDBO算法有效地解决了传统DBO的局限性,改善了CNN参数调整.
- 这项工作有助于神经网络技术及其硬件应用的进步.
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