Abstract
This study presents a hybrid model for classifying handwritten digits in the MNIST dataset, combining convolutional neural networks (CNNs) with a multi-well Hopfield network. The approach employs a CNN to extract high-dimensional features from input images, which are then clustered into class-specific prototypes using k-means clustering. These prototypes serve as attractors in a multi-well energy landscape, where a Hopfield network performs classification by minimizing an energy function that balances feature similarity and class this http URL model's design enables robust handling of intraclass variability, such as diverse handwriting styles, while providing an interpretable framework through its energy-based decision process. Through systematic optimization of the CNN architecture and the number of wells, the model achieves a high test accuracy of 99.2% on 10,000 MNIST images, demonstrating its effectiveness for image classification tasks. The findings highlight the critical role of deep feature extraction and sufficient prototype coverage in achieving high performance, with potential for broader applications in pattern recognition.
Abstract (translated)
这项研究提出了一种用于在MNIST数据集上分类手写数字的混合模型，该模型结合了卷积神经网络（CNN）与多阱霍普菲尔德网络。这种方法利用CNN从输入图像中提取高维特征，并使用k均值聚类将这些特征聚类为特定于每个类别的原型。这些原型作为具有多个能量陷阱的能量景观中的吸引子，在其中霍普菲尔德网络通过最小化一个平衡了特征相似性和类别归属的能函数来进行分类。该模型的设计能够稳健地处理同一类别内的变化，例如多样的手写风格，并且由于其基于能量的决策过程提供了可解释性框架。 通过对CNN架构和阱的数量进行系统优化，该模型在10,000张MNIST图像上达到了99.2%的高测试准确率，证明了它对于图像分类任务的有效性。研究结果强调了深度特征提取以及充分原型覆盖对于实现高性能的关键作用，并且其潜在的应用范围可能更广泛，在模式识别领域具有潜力。
URL
https://arxiv.org/abs/2507.08766
