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Neural NetworksSCIE

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

中科院分區(qū)
1區(qū)
CiteScore分區(qū)
Q1
JCR分區(qū)
Q1

基本信息：: ISSN：0893-6080; E-ISSN：1879-2782; 是否OA：未開放; 是否預(yù)警：否; TOP期刊：是

出版信息：: 出版地區(qū)：ENGLAND; 出版商：Elsevier Ltd; 出版語言：English; 出版周期：Monthly; 出版年份：1988; 研究方向：工程技術(shù)-計算機(jī)：人工智能

評價信息：: 影響因子：6; H-index：128; CiteScore指數(shù)：13.9; SJR指數(shù)：2.605; SNIP指數(shù)：2.442

發(fā)文數(shù)據(jù)：: Gold OA文章占比：19.75%; 研究類文章占比：98.15%; 年發(fā)文量：595; 自引率：0.0769...; 開源占比：0.1207; 出版撤稿占比：0; 出版國人文章占比：0.35; OA被引用占比：0.0623...

投稿咨詢加急見刊

英文簡介期刊介紹 CiteScore數(shù)據(jù) 中科院SCI分區(qū) JCR分區(qū) 發(fā)文數(shù)據(jù) 常見問題

英文簡介Neural Networks期刊介紹

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.

期刊簡介Neural Networks期刊介紹

《Neural Networks》自1988出版以來，是一本計算機(jī)科學(xué)優(yōu)秀雜志。致力于發(fā)表原創(chuàng)科學(xué)研究結(jié)果，并為計算機(jī)科學(xué)各個領(lǐng)域的原創(chuàng)研究提供一個展示平臺，以促進(jìn)計算機(jī)科學(xué)領(lǐng)域的的進(jìn)步。該刊鼓勵先進(jìn)的、清晰的闡述，從廣泛的視角提供當(dāng)前感興趣的研究主題的新見解，或?qū)彶槎嗄陙砟硞€重要領(lǐng)域的所有重要發(fā)展。該期刊特色在于及時報道計算機(jī)科學(xué)領(lǐng)域的最新進(jìn)展和新發(fā)現(xiàn)新突破等。該刊近一年未被列入預(yù)警期刊名單，目前已被權(quán)威數(shù)據(jù)庫SCIE收錄，得到了廣泛的認(rèn)可。

該期刊投稿重要關(guān)注點(diǎn)：

預(yù)計審稿時間：約12.0個月約10.5周
國際TOP期刊
計算機(jī)科學(xué)
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
SCIE
中科院1區(qū)
非預(yù)警

Cite Score數(shù)據(jù)（2024年最新版）Neural Networks Cite Score數(shù)據(jù)

CiteScore：13.9
SJR：2.605
SNIP：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%

CiteScore 是由Elsevier(愛思唯爾)推出的另一種評價期刊影響力的文獻(xiàn)計量指標(biāo)。反映出一家期刊近期發(fā)表論文的年篇均引用次數(shù)。CiteScore以Scopus數(shù)據(jù)庫中收集的引文為基礎(chǔ)，針對的是前四年發(fā)表的論文的引文。CiteScore的意義在于，它可以為學(xué)術(shù)界提供一種新的、更全面、更客觀地評價期刊影響力的方法，而不僅僅是通過影響因子(IF)這一單一指標(biāo)來評價。

歷年Cite Score趨勢圖

中科院SCI分區(qū)Neural Networks 中科院分區(qū)

中科院 2023年12月升級版 綜述期刊：否 Top期刊：是

大類學(xué)科	分區(qū)	小類學(xué)科	分區(qū)
計算機(jī)科學(xué)	1區(qū)	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計算機(jī)：人工智能 NEUROSCIENCES 神經(jīng)科學(xué)	2區(qū) 2區(qū)

中科院分區(qū)表 是以客觀數(shù)據(jù)為基礎(chǔ)，運(yùn)用科學(xué)計量學(xué)方法對國際、國內(nèi)學(xué)術(shù)期刊依據(jù)影響力進(jìn)行等級劃分的期刊評價標(biāo)準(zhǔn)。它為我國科研、教育機(jī)構(gòu)的管理人員、科研工作者提供了一份評價國際學(xué)術(shù)期刊影響力的參考數(shù)據(jù)，得到了全國各地高校、科研機(jī)構(gòu)的廣泛認(rèn)可。

中科院分區(qū)表 將所有期刊按照一定指標(biāo)劃分為1區(qū)、2區(qū)、3區(qū)、4區(qū)四個層次，類似于“優(yōu)、良、及格”等。最開始，這個分區(qū)只是為了方便圖書管理及圖書情報領(lǐng)域的研究和期刊評估。之后中科院分區(qū)逐步發(fā)展成為了一種評價學(xué)術(shù)期刊質(zhì)量的重要工具。

歷年中科院分區(qū)趨勢圖

JCR分區(qū)Neural Networks JCR分區(qū)

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%

JCR分區(qū)的優(yōu)勢在于它可以幫助讀者對學(xué)術(shù)文獻(xiàn)質(zhì)量進(jìn)行評估。不同學(xué)科的文章引用量可能存在較大的差異，此時單獨(dú)依靠影響因子(IF)評價期刊的質(zhì)量可能是存在一定問題的。因此，JCR將期刊按照學(xué)科門類和影響因子分為不同的分區(qū)，這樣讀者可以根據(jù)自己的研究領(lǐng)域和需求選擇合適的期刊。

歷年影響因子趨勢圖

發(fā)文數(shù)據(jù)

2023-2024 年國家/地區(qū)發(fā)文量統(tǒng)計

國家/地區(qū)數(shù)量
CHINA MAINLAND408
USA133
England58
Japan55
Australia39
Spain33
South Korea32
Italy30
GERMANY (FED REP GER)28
France27

本刊中國學(xué)者近年發(fā)表論文

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