An Interpretable Constructive Algorithm for Incremental Random Weight Neural Networks and Its Application

In this article, we aim to offer an interpretable learning paradigm for incremental random weight neural networks (IRWNNs). IRWNNs have become a hot research direction of neural network algorithms due to their ease of deployment and fast learning speed. However, existing IRWNNs have difficulty expla...

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Bibliographic Details
Published in:	IEEE transactions on industrial informatics Vol. 20; no. 12; pp. 13622 - 13632
Main Authors:	Nan, Jing, Dai, Wei, Yuan, Guan, Zhou, Ping
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.12.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Artificial neural networks Convergence Data modeling Datasets Geometry Informatics interpretable constructive algorithm Machine learning Mathematical models Network architecture Neural networks neural networks (NNs) Parameters random algorithms Reviews spatial geometric information Task complexity
ISSN:	1551-3203, 1941-0050
Online Access:	Get full text
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