论文标题
树突网：用于分类，回归和系统标识的白色盒子模块
Dendrite Net: A White-Box Module for Classification, Regression, and System Identification
论文作者
论文摘要
生物树突计算的模拟对于人工智能（AI）的发展至关重要。本文介绍了一种基本的机器学习算法，称为Dendrite Net或DD，就像支持向量机（SVM）或多层Perceptron（MLP）一样。 DD的主要概念是，如果输出的逻辑表达式包含输入（以及$ \ backslash $或$ \ backslash $ not），该算法可以在学习后识别此类。实验和主要结果：DD，一种白盒机器学习算法DD显示了黑盒系统的出色系统识别性能。其次，通过九种现实世界的应用来验证，DD相对于模仿神经元的细胞体（细胞体Net）进行回归的MLP结构带来了更好的概括能力。第三，通过MNIST和时尚持续数据集，已证实DD在训练损失下显示出比细胞体网更高的测试精度进行分类。模块的数量可以有效地调整DD的逻辑表达能力，从而避免过度拟合，并可以轻松获得具有出色概括能力的模型。最后，在Matlab和Pytorch（Python）中重复实验表明，DD在时期和前向传播中均高于细胞体网。本文的主要贡献是具有白色框属性的基本机器学习算法（DD），可控制的精度，以提高概括能力和降低计算复杂性。 DD不仅可以用于广义工程，而且DD具有巨大的发展潜力，作为深度学习的模块。 DD代码可在GitHub上获得：https：//github.com/liugang1234567/gang-neuron。
The simulation of biological dendrite computations is vital for the development of artificial intelligence (AI). This paper presents a basic machine learning algorithm, named Dendrite Net or DD, just like Support Vector Machine (SVM) or Multilayer Perceptron (MLP). DD's main concept is that the algorithm can recognize this class after learning, if the output's logical expression contains the corresponding class's logical relationship among inputs (and$\backslash$or$\backslash$not). Experiments and main results: DD, a white-box machine learning algorithm, showed excellent system identification performance for the black-box system. Secondly, it was verified by nine real-world applications that DD brought better generalization capability relative to MLP architecture that imitated neurons' cell body (Cell body Net) for regression. Thirdly, by MNIST and FASHION-MNIST datasets, it was verified that DD showed higher testing accuracy under greater training loss than Cell body Net for classification. The number of modules can effectively adjust DD's logical expression capacity, which avoids over-fitting and makes it easy to get a model with outstanding generalization capability. Finally, repeated experiments in MATLAB and PyTorch (Python) demonstrated that DD was faster than Cell body Net both in epoch and forward-propagation. The main contribution of this paper is the basic machine learning algorithm (DD) with a white-box attribute, controllable precision for better generalization capability, and lower computational complexity. Not only can DD be used for generalized engineering, but DD has vast development potential as a module for deep learning. DD code is available at GitHub: https://github.com/liugang1234567/Gang-neuron .
