用静电场势能直接生成神经网络权重，无需训练即可快速部署。
Experiment on creating a neural network with weights determined by the potential of a simulated electrostatic field
- 通过模拟静电场势能直接设定神经网络第一层权重
- 在MNIST数据集上验证了该方法的有效性，结果功能可行
- 适合需要快速初始化权重的场景，如边缘计算或实时系统
本文探索了在不进行解析计算和不使用训练算法的情况下，利用静电场的势能参数确定神经网络权重和阈值的可能性。研究基于采用度量识别方法的神经网络架构，静电场在Builder C++环境中模拟，同一环境构建了基于度量识别方法的神经网络，其第一层神经元权重由模拟静电场的势能值决定。在不同初始条件下，使用MNIST测试数据集评估了该方法在模拟系统中的有效性。实验结果表明该方法具有功能性可行性。实现表明，神经网络可几乎瞬间从静电场获取权重值，无需解析计算、漫长的训练过程或大规模训练数据集。
原文摘要 · Abstract (English)
This paper explores the possibility of determining the weights and thresholds of a neural network using the potential -- a parameter of an electrostatic field -- without analytical calculations and without applying training algorithms. The work is based on neural network architectures employing metric recognition methods. The electrostatic field is simulated in the Builder C++ environment. In the same environment, a neural network based on metric recognition methods is constructed, with the weights of the first-layer neurons determined by the values of the potentials of the simulated electrostatic field. The effectiveness of the resulting neural network within the simulated system is evaluated using the MNIST test dataset under various initial conditions of the simulated system. The results demonstrated functional viability. The implementation of this approach shows that a neural network can obtain weight values almost instantaneously from the electrostatic field, without the need for analytical computations, lengthy training procedures, or massive training datasets.
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