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The simulation of biological dendrite computations is vital for thedevelopment of artificial intelligence (AI). This paper presents a basicmachine learning algorithm, named Dendrite Net or DD, just like Support VectorMachine (SVM) or Multilayer Perceptron (MLP). DD's main concept is that thealgorithm can recognize this class after learning, if the output's logicalexpression contains the corresponding class's logical relationship among inputs(and$\backslash$or$\backslash$not). Experiments and main results: DD, awhite-box machine learning algorithm, showed excellent system identificationperformance for the black-box system. Secondly, it was verified by ninereal-world applications that DD brought better generalization capabilityrelative to MLP architecture that imitated neurons' cell body (Cell body Net)for regression. Thirdly, by MNIST and FASHION-MNIST datasets, it was verifiedthat DD showed higher testing accuracy under greater training loss than Cellbody Net for classification. The number of modules can effectively adjust DD'slogical expression capacity, which avoids over-fitting and makes it easy to geta model with outstanding generalization capability. Finally, repeatedexperiments in MATLAB and PyTorch (Python) demonstrated that DD was faster thanCell body Net both in epoch and forward-propagation. The main contribution ofthis paper is the basic machine learning algorithm (DD) with a white-boxattribute, controllable precision for better generalization capability, andlower computational complexity. Not only can DD be used for generalizedengineering, but DD has vast development potential as a module for deeplearning. DD code is available at GitHub:https://github.com/liugang1234567/Gang-neuron .

Dendrite Net: A White-Box Module for Classification, Regression, and System Identification