Neural Networks SCIE

國際簡稱：NEURAL NETWORKS 參考譯名：神經(jīng)網(wǎng)絡(luò)

主要研究方向：工程技術(shù)-計(jì)算機(jī)：人工智能非預(yù)警期刊審稿周期：約12.0個(gè)月約10.5周

《神經(jīng)網(wǎng)絡(luò)》(Neural Networks)是一本由Elsevier Ltd出版的以工程技術(shù)-計(jì)算機(jī)：人工智能為研究特色的國際期刊，發(fā)表該領(lǐng)域相關(guān)的原創(chuàng)研究文章、評論文章和綜述文章，及時(shí)報(bào)道該領(lǐng)域相關(guān)理論、實(shí)踐和應(yīng)用學(xué)科的最新發(fā)現(xiàn)，旨在促進(jìn)該學(xué)科領(lǐng)域科學(xué)信息的快速交流。該期刊是一本未開放期刊，近三年沒有被列入預(yù)警名單。該期刊享有很高的科學(xué)聲譽(yù)，影響因子不斷增加，發(fā)行量也同樣高。

1區(qū) 中科院分區(qū)
Q1 JCR分區(qū)
595 年發(fā)文量
6 IF影響因子
未開放 是否OA
128 H-index
1988 創(chuàng)刊年份
Monthly 出版周期
English 出版語言

Neural Networks is the archival journal of the world's three oldest neural modeling societies: the International Neural Network Society (INNS), the European Neural Network Society (ENNS), and the Japanese Neural Network Society (JNNS). A subscription to the journal is included with membership in each of these societies.

Neural Networks provides a forum for developing and nurturing an international community of scholars and practitioners who are interested in all aspects of neural networks and related approaches to computational intelligence. Neural Networks welcomes high quality submissions that contribute to the full range of neural networks research, from behavioral and brain modeling, learning algorithms, through mathematical and computational analyses, to engineering and technological applications of systems that significantly use neural network concepts and techniques. This uniquely broad range facilitates the cross-fertilization of ideas between biological and technological studies, and helps to foster the development of the interdisciplinary community that is interested in biologically-inspired computational intelligence. Accordingly, Neural Networks editorial board represents experts in fields including psychology, neurobiology, computer science, engineering, mathematics, and physics. The journal publishes articles, letters and reviews, as well as letters to the editor, editorials, current events, software surveys, and patent information. Articles are published in one of five sections: Cognitive Science, Neuroscience, Learning Systems, Mathematical and Computational Analysis, Engineering and Applications.

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Neural Networks期刊信息

ISSN：0893-6080
出版語言：English
是否OA：未開放
E-ISSN：1879-2782
出版地區(qū)：ENGLAND
是否預(yù)警：否
出版商：Elsevier Ltd
出版周期：Monthly
創(chuàng)刊時(shí)間：1988
開源占比：0.1207
Gold OA文章占比：19.75%
OA被引用占比：0.0623...
出版國人文章占比：0.35
出版撤稿占比：0
研究類文章占比：98.15%

Neural Networks CiteScore評價(jià)數(shù)據(jù)（2024年最新版）

CiteScore

SJR

SNIP

CiteScore 指數(shù)

13.9

2.605

2.442

學(xué)科類別	分區(qū)	排名	百分位
大類：Neuroscience 小類：Cognitive Neuroscience	Q1	4 / 115	96%
大類：Neuroscience 小類：Artificial Intelligence	Q1	35 / 350	90%

名詞解釋：CiteScore 是衡量期刊所發(fā)表文獻(xiàn)的平均受引用次數(shù)，是在 Scopus 中衡量期刊影響力的另一個(gè)指標(biāo)。當(dāng)年CiteScore 的計(jì)算依據(jù)是期刊最近4年（含計(jì)算年度）的被引次數(shù)除以該期刊近四年發(fā)表的文獻(xiàn)數(shù)。例如，2022年的 CiteScore 計(jì)算方法為：2022年的 CiteScore =2019-2022年收到的對2019-2022年發(fā)表的文件的引用數(shù)量÷2019-2022年發(fā)布的文獻(xiàn)數(shù)量注：文獻(xiàn)類型包括：文章、評論、會議論文、書籍章節(jié)和數(shù)據(jù)論文。

Neural Networks中科院評價(jià)數(shù)據(jù)

中科院 2023年12月升級版

Top期刊	綜述期刊	大類學(xué)科		小類學(xué)科
是	否	計(jì)算機(jī)科學(xué)	1區(qū)	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī)：人工智能 NEUROSCIENCES 神經(jīng)科學(xué)	2區(qū) 2區(qū)

中科院 2022年12月升級版

Top期刊	綜述期刊	大類學(xué)科		小類學(xué)科
是	否	計(jì)算機(jī)科學(xué)	1區(qū)	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī)：人工智能 NEUROSCIENCES 神經(jīng)科學(xué)	2區(qū) 2區(qū)

中科院 2021年12月舊的升級版

Top期刊	綜述期刊	大類學(xué)科		小類學(xué)科
否	否	計(jì)算機(jī)科學(xué)	2區(qū)	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī)：人工智能 NEUROSCIENCES 神經(jīng)科學(xué)	2區(qū) 2區(qū)

中科院 2021年12月基礎(chǔ)版

Top期刊	綜述期刊	大類學(xué)科		小類學(xué)科
否	否	工程技術(shù)	2區(qū)	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī)：人工智能 NEUROSCIENCES 神經(jīng)科學(xué)	2區(qū) 2區(qū)

中科院 2021年12月升級版

Top期刊	綜述期刊	大類學(xué)科		小類學(xué)科
否	否	計(jì)算機(jī)科學(xué)	2區(qū)	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī)：人工智能 NEUROSCIENCES 神經(jīng)科學(xué)	2區(qū) 2區(qū)

中科院 2020年12月舊的升級版

Top期刊	綜述期刊	大類學(xué)科		小類學(xué)科
否	否	計(jì)算機(jī)科學(xué)	2區(qū)	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī)：人工智能 NEUROSCIENCES 神經(jīng)科學(xué)	2區(qū) 2區(qū)

Neural Networks JCR評價(jià)數(shù)據(jù)（2023-2024年最新版）

按JIF指標(biāo)學(xué)科分區(qū)	收錄子集	分區(qū)	排名	百分位
學(xué)科：COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE	SCIE	Q1	38 / 197	81%
學(xué)科：NEUROSCIENCES	SCIE	Q1	34 / 310	89.2%

按JCI指標(biāo)學(xué)科分區(qū)	收錄子集	分區(qū)	排名	百分位
學(xué)科：COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE	SCIE	Q1	28 / 198	86.11%
學(xué)科：NEUROSCIENCES	SCIE	Q1	32 / 310	89.84%

Neural Networks歷年數(shù)據(jù)統(tǒng)計(jì)

影響因子

中科院分區(qū)

Neural Networks中國學(xué)者發(fā)文選摘

1、Lifelong learning with Shared and Private Latent Representations learned through synaptic intelligence
Author: Yang, Yang; Huang, Jie; Hu, Dexiu

Journal: NEURAL NETWORKS. 2023; Vol. 163, Issue , pp. 165-177. DOI: 10.1016/j.neunet.2023.04.005
2、Collaborative-guided spectral abundance learning with bilinear mixing model for hyperspectral subpixel target detection
Author: Zhu, Dehui; Du, Bo; Hu, Meiqi; Dong, Yanni; Zhang, Liangpei

Journal: NEURAL NETWORKS. 2023; Vol. 163, Issue , pp. 205-218. DOI: 10.1016/j.neunet.2023.02.002
3、Unsupervised graph-level representation learning with hierarchical contrasts
Author: Ju, Wei; Gu, Yiyang; Luo, Xiao; Wang, Yifan; Yuan, Haochen; Zhong, Huasong; Zhang, Ming

Journal: NEURAL NETWORKS. 2023; Vol. 158, Issue , pp. 359-368. DOI: 10.1016/j.neunet.2022.11.019
4、Monte Carlo Ensemble Neural Network for the diagnosis of Alzheimer's disease
Author: Liu, Chaoqiang; Huang, Fei; Qiu, Anqi

Journal: NEURAL NETWORKS. 2023; Vol. 159, Issue , pp. 14-24. DOI: 10.1016/j.neunet.2022.10.032
5、Factorizing time-heterogeneous Markov transition for temporal recommendation?
Author: Wen, Wen; Wang, Wencui; Hao, Zhifeng; Cai, Ruichu

Journal: NEURAL NETWORKS. 2023; Vol. 159, Issue , pp. 84-96. DOI: 10.1016/j.neunet.2022.11.032
6、Efficient Perturbation Inference and Expandable Network for continual learning
Author: Du, Fei; Yang, Yun; Zhao, Ziyuan; Zeng, Zeng

Journal: NEURAL NETWORKS. 2023; Vol. 159, Issue , pp. 97-106. DOI: 10.1016/j.neunet.2022.10.030
7、Variable three-term conjugate gradient method for training artificial neural networks
Author: Kim, Hansu; Wang, Chuxuan; Byun, Hyoseok; Hu, Weifei; Kim, Sanghyuk; Jiao, Qing; Lee, Tah Hee

Journal: NEURAL NETWORKS. 2023; Vol. 159, Issue , pp. 125-136. DOI: 10.1016/j.neunet.2022.12.001
8、Representation learning for continuous action spaces is beneficial for efficient policy learning
Author: Zhao, Tingting; Wang, Ying; Sun, Wei; Chen, Yarui; Niu, Gang; Sugiyama, Masashi

Journal: NEURAL NETWORKS. 2023; Vol. 159, Issue , pp. 137-152. DOI: 10.1016/j.neunet.2022.12.009