Qinwei Fan
Overview
Explore the profile of Qinwei Fan including associated specialties, affiliations and a list of published articles.
Author names and details appear as published. Due to indexing inconsistencies, multiple individuals may share a name, and a single author may have variations. MedLuna displays this data as publicly available, without modification or verification
Snapshot
Snapshot
Articles
5
Citations
9
Followers
0
Related Specialties
Related Specialties
Top 10 Co-Authors
Top 10 Co-Authors
Published In
Affiliations
Affiliations
Soon will be listed here.
Recent Articles
1.
Ji D, Fan Q, Dong Q, Liu Y
Neural Netw
. 2024 Aug;
179:106599.
PMID: 39142176
Dealing with high-dimensional problems has always been a key and challenging issue in the field of fuzzy systems. Traditional Takagi-Sugeno-Kang (TSK) fuzzy systems face the challenges of the curse of...
2.
Fan Q, Kang Q, Zurada J, Huang T, Xu D
IEEE Trans Neural Netw Learn Syst
. 2023 Oct;
35(12):18687-18701.
PMID: 37847629
In this article, we investigate the boundedness and convergence of the online gradient method with the smoothing group regularization for the sigma-pi-sigma neural network (SPSNN). This enhances the sparseness of...
3.
Zhang K, Gu S, Wu Y, Fan Q, Zhu C
Nanotechnology
. 2020 Feb;
31(21):215702.
PMID: 32032008
Pyramidal SnO/CeO nano-heterojunction photocatalysts were successfully synthesized via a facile hydrothermal method. The structural characterization was investigated by using common characterization tools. The content of SnO affected the morphology and...
4.
Fan Q, Wu W, Zurada J
Springerplus
. 2016 Apr;
5:295.
PMID: 27066332
This paper presents new theoretical results on the backpropagation algorithm with smoothing [Formula: see text] regularization and adaptive momentum for feedforward neural networks with a single hidden layer, i.e., we...
5.
Batch gradient method with smoothing L1/2 regularization for training of feedforward neural networks
Wu W, Fan Q, Zurada J, Wang J, Yang D, Liu Y
Neural Netw
. 2013 Dec;
50:72-8.
PMID: 24291693
The aim of this paper is to develop a novel method to prune feedforward neural networks by introducing an L1/2 regularization term into the error function. This procedure forces weights...