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

The simulation of biological dendrite computations is vital for the development of artificial intelligence (AI). This article presents a basic machine-learning (ML) algorithm, called Dendrite Net or DD, just like the support vector machine (SVM) or multilayer perceptron (MLP). DD's main concept...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on cybernetics Ročník 52; číslo 12; s. 13774 - 13787
Hlavní autori: Liu, Gang, Wang, Jing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Artificial intelligence
artificial intelligence (AI)
Biological computing
Biological system modeling
Classification
Computational complexity
Computer architecture
Controllability
Data mining
Deep learning
Dendrites (neurons)
Dendritic structure
engineering
Machine learning
machine learning (ML)
Microprocessors
Modules
Multilayer perceptrons
pattern recognition
Support vector machines
System identification
Training
ISSN:2168-2267, 2168-2275, 2168-2275
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The simulation of biological dendrite computations is vital for the development of artificial intelligence (AI). This article presents a basic machine-learning (ML) algorithm, called Dendrite Net or DD, just like the 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 <inline-formula> <tex-math notation="LaTeX">\backslash </tex-math></inline-formula> or <inline-formula> <tex-math notation="LaTeX">\backslash </tex-math></inline-formula> not). Experiments and main results: DD, a white-box ML algorithm, showed excellent system identification performance for the black-box system. Second, it was verified by nine real-world applications that DD brought better generalization capability relative to the MLP architecture that imitated neurons' cell body (Cell body Net) for regression. Third, by MNIST and FASHION-MNIST datasets, it was verified that DD showed higher testing accuracy under greater training loss than the cell body net for classification. The number of modules can effectively adjust DD's logical expression capacity, which avoids overfitting 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 forwardpropagation. The main contribution of this article is the basic ML 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 https://github.com/liugang1234567/Gang-neuron .
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2021.3124328