Design of Convolutional Fuzzy Neural Network Classifiers

Jiying Men; Wei Huang; Jinsong Wang

doi:10.1109/ICAICE51518.2020.00065

Design of Convolutional Fuzzy Neural Network Classifiers

Jiying Men, Wei Huang^*, Jinsong Wang

^*此作品的通讯作者

Tianjin University of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

In this paper, we propose a convolutional fuzzy neural network classification to alleviate the problems of processing high-dimensional data and low computational efficiency in traditional convolutional neural networks. The model proposes a convolution fuzzy C-means algorithm, in the meanwhile uses the L2-norm regularization method to estimate parameters, so that it has better generalization ability. The experimental results indicate that the proposed CFNNCs have excellent performance in classification accuracy than classic classification models such as SVM, RVM, KNN, and the experimental accuracy can be maintained above 90%.

源语言	英语
主期刊名	Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020
出版商	Institute of Electrical and Electronics Engineers Inc.
页	306-309
页数	4
ISBN（电子版）	9781728191461
DOI	https://doi.org/10.1109/ICAICE51518.2020.00065
出版状态	已出版 - 10月 2020
已对外发布	是
活动	2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020 - Beijing, 中国期限: 23 10月 2020 → 25 10月 2020

出版系列

姓名	Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020

会议

会议	2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020
国家/地区	中国
市	Beijing
时期	23/10/20 → 25/10/20

访问文件

10.1109/ICAICE51518.2020.00065

其它文件与链接

链接到 Scopus 的出版物

引用此

Men, J., Huang, W., & Wang, J. (2020). Design of Convolutional Fuzzy Neural Network Classifiers. 在 Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020 (页码 306-309). 文章 9361333 (Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAICE51518.2020.00065

Men, Jiying ; Huang, Wei ; Wang, Jinsong. / Design of Convolutional Fuzzy Neural Network Classifiers. Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 页码 306-309 (Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020).

@inproceedings{c4b84c0549f0407bbf43462d58ef025a,

title = "Design of Convolutional Fuzzy Neural Network Classifiers",

abstract = "In this paper, we propose a convolutional fuzzy neural network classification to alleviate the problems of processing high-dimensional data and low computational efficiency in traditional convolutional neural networks. The model proposes a convolution fuzzy C-means algorithm, in the meanwhile uses the L2-norm regularization method to estimate parameters, so that it has better generalization ability. The experimental results indicate that the proposed CFNNCs have excellent performance in classification accuracy than classic classification models such as SVM, RVM, KNN, and the experimental accuracy can be maintained above 90%.",

keywords = "L2-norm regularization, convolutional neural network, fuzzy rules",

author = "Jiying Men and Wei Huang and Jinsong Wang",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020 ; Conference date: 23-10-2020 Through 25-10-2020",

year = "2020",

month = oct,

doi = "10.1109/ICAICE51518.2020.00065",

language = "English",

series = "Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "306--309",

booktitle = "Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020",

address = "United States",

}

Men, J, Huang, W & Wang, J 2020, Design of Convolutional Fuzzy Neural Network Classifiers. 在 Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020., 9361333, Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020, Institute of Electrical and Electronics Engineers Inc., 页码 306-309, 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020, Beijing, 中国, 23/10/20. https://doi.org/10.1109/ICAICE51518.2020.00065

Design of Convolutional Fuzzy Neural Network Classifiers. / Men, Jiying; Huang, Wei; Wang, Jinsong.
Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 页码 306-309 9361333 (Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Design of Convolutional Fuzzy Neural Network Classifiers

AU - Men, Jiying

AU - Huang, Wei

AU - Wang, Jinsong

PY - 2020/10

Y1 - 2020/10

N2 - In this paper, we propose a convolutional fuzzy neural network classification to alleviate the problems of processing high-dimensional data and low computational efficiency in traditional convolutional neural networks. The model proposes a convolution fuzzy C-means algorithm, in the meanwhile uses the L2-norm regularization method to estimate parameters, so that it has better generalization ability. The experimental results indicate that the proposed CFNNCs have excellent performance in classification accuracy than classic classification models such as SVM, RVM, KNN, and the experimental accuracy can be maintained above 90%.

AB - In this paper, we propose a convolutional fuzzy neural network classification to alleviate the problems of processing high-dimensional data and low computational efficiency in traditional convolutional neural networks. The model proposes a convolution fuzzy C-means algorithm, in the meanwhile uses the L2-norm regularization method to estimate parameters, so that it has better generalization ability. The experimental results indicate that the proposed CFNNCs have excellent performance in classification accuracy than classic classification models such as SVM, RVM, KNN, and the experimental accuracy can be maintained above 90%.

KW - L2-norm regularization

KW - convolutional neural network

KW - fuzzy rules

UR - http://www.scopus.com/inward/record.url?scp=85102660704&partnerID=8YFLogxK

U2 - 10.1109/ICAICE51518.2020.00065

DO - 10.1109/ICAICE51518.2020.00065

M3 - Conference contribution

AN - SCOPUS:85102660704

T3 - Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020

SP - 306

EP - 309

BT - Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020

Y2 - 23 October 2020 through 25 October 2020

ER -

Men J, Huang W, Wang J. Design of Convolutional Fuzzy Neural Network Classifiers. 在 Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 页码 306-309. 9361333. (Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020). doi: 10.1109/ICAICE51518.2020.00065

Design of Convolutional Fuzzy Neural Network Classifiers

摘要

出版系列

会议

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指纹

引用此