Yang-Yin Lin

1.3k total citations
23 papers, 1.1k citations indexed

About

Yang-Yin Lin is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Control and Systems Engineering. According to data from OpenAlex, Yang-Yin Lin has authored 23 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 10 papers in Cognitive Neuroscience and 5 papers in Control and Systems Engineering. Recurrent topics in Yang-Yin Lin's work include Neural Networks and Applications (14 papers), Fuzzy Logic and Control Systems (13 papers) and EEG and Brain-Computer Interfaces (10 papers). Yang-Yin Lin is often cited by papers focused on Neural Networks and Applications (14 papers), Fuzzy Logic and Control Systems (13 papers) and EEG and Brain-Computer Interfaces (10 papers). Yang-Yin Lin collaborates with scholars based in Taiwan, Australia and India. Yang-Yin Lin's co-authors include Chin‐Teng Lin, Jyh‐Yeong Chang, Chia‐Feng Juang, Yuting Liu, Shang-Lin Wu, Chun‐Hsiang Chuang, Nikhil R. Pal, Tsung-Yu Hsieh, Gary G. Yen and Yu–Kai Wang and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Fuzzy Systems and Fuzzy Sets and Systems.

In The Last Decade

Yang-Yin Lin

23 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yang-Yin Lin Taiwan 12 755 318 154 149 94 23 1.1k
Jyh‐Yeong Chang Taiwan 16 569 0.8× 219 0.7× 415 2.7× 199 1.3× 60 0.6× 55 1.4k
Nor Azlina Ab. Aziz Malaysia 18 363 0.5× 72 0.2× 118 0.8× 162 1.1× 51 0.5× 94 946
Sohrab Khanmohammadi Iran 19 188 0.2× 338 1.1× 105 0.7× 272 1.8× 18 0.2× 101 1.3k
Woei Wan Tan Singapore 15 891 1.2× 408 1.3× 52 0.3× 94 0.6× 343 3.6× 45 1.3k
Ning Xiong Sweden 17 513 0.7× 141 0.4× 46 0.3× 79 0.5× 6 0.1× 80 913
M. A. H. Akhand Bangladesh 16 458 0.6× 73 0.2× 135 0.9× 86 0.6× 9 0.1× 114 1.3k
Hung‐Yuan Chung Taiwan 15 336 0.4× 664 2.1× 61 0.4× 123 0.8× 23 0.2× 66 1.1k
Shing Chiang Tan Malaysia 21 448 0.6× 168 0.5× 67 0.4× 146 1.0× 6 0.1× 104 1.2k
Peter Funk Sweden 16 507 0.7× 145 0.5× 83 0.5× 16 0.1× 7 0.1× 87 1.0k
Pratyusha Rakshit India 13 435 0.6× 115 0.4× 89 0.6× 33 0.2× 11 0.1× 69 754

Countries citing papers authored by Yang-Yin Lin

Since Specialization
Citations

This map shows the geographic impact of Yang-Yin Lin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yang-Yin Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yang-Yin Lin more than expected).

Fields of papers citing papers by Yang-Yin Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yang-Yin Lin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yang-Yin Lin. The network helps show where Yang-Yin Lin may publish in the future.

Co-authorship network of co-authors of Yang-Yin Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Yang-Yin Lin. A scholar is included among the top collaborators of Yang-Yin Lin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yang-Yin Lin. Yang-Yin Lin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lin, Chin‐Teng, Yuting Liu, Chun‐Hsiang Chuang, et al.. (2019). An Adaptive Subspace Self-Organizing Map (ASSOM) Imbalanced Learning and Its Applications in EEG.. arXiv (Cornell University). 1 indexed citations
2.
Lin, Chin‐Teng, Tsung-Yu Hsieh, Yuting Liu, et al.. (2017). Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets. IEEE Transactions on Knowledge and Data Engineering. 30(5). 950–962. 51 indexed citations
3.
Liu, Yuting, Shang-Lin Wu, Yang-Yin Lin, et al.. (2016). Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network. 16 indexed citations
4.
Wu, Shang-Lin, Yuting Liu, Yang-Yin Lin, et al.. (2016). A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application. UTS ePRESS (University of Technology Sydney). 2495–2500. 5 indexed citations
5.
6.
Liu, Yuting, Yang-Yin Lin, Shang-Lin Wu, Tsung-Yu Hsieh, & Chin‐Teng Lin. (2015). Assessment of Mental Fatigue: An EEG-Based Forecasting System for Driving Safety. 3233–3238. 6 indexed citations
7.
8.
Lin, Chin‐Teng, Tsung-Yu Hsieh, Yuting Liu, Shang-Lin Wu, & Yang-Yin Lin. (2015). A Novel Mechanism to Fuse Various Sub-Aspect Brain-Computer Interface (BCI) Systems with PSO for Motor Imagery Task. 3223–3228. 3 indexed citations
9.
Liu, Yuting, Yang-Yin Lin, Shang-Lin Wu, et al.. (2014). EEG-based driving fatigue prediction system using functional-link-based fuzzy neural network. 563. 4109–4113. 11 indexed citations
10.
Lin, Chin‐Teng, Nikhil R. Pal, Shang-Lin Wu, Yuting Liu, & Yang-Yin Lin. (2014). An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination. IEEE Transactions on Neural Networks and Learning Systems. 26(7). 1442–1455. 68 indexed citations
11.
Lin, Yang-Yin, Jyh‐Yeong Chang, & Chin‐Teng Lin. (2013). A TSK-Type-Based Self-Evolving Compensatory Interval Type-2 Fuzzy Neural Network (TSCIT2FNN) and Its Applications. IEEE Transactions on Industrial Electronics. 61(1). 447–459. 132 indexed citations
12.
Lin, Yang-Yin, Jyh‐Yeong Chang, Nikhil R. Pal, & Chin‐Teng Lin. (2013). A Mutually Recurrent Interval Type-2 Neural Fuzzy System (MRIT2NFS) With Self-Evolving Structure and Parameters. IEEE Transactions on Fuzzy Systems. 21(3). 492–509. 75 indexed citations
13.
Lin, Yang-Yin, Jyh‐Yeong Chang, & Chin‐Teng Lin. (2012). Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-Evolving Fuzzy Neural Network. IEEE Transactions on Neural Networks and Learning Systems. 24(2). 310–321. 153 indexed citations
14.
Lin, Chin‐Teng, et al.. (2011). Neuro-fuzzy system design using differential evolution with local information. 1003–1006. 5 indexed citations
15.
Juang, Chia‐Feng, et al.. (2011). Dynamic system modeling using a recurrent interval-valued fuzzy neural network and its hardware implementation. Fuzzy Sets and Systems. 179(1). 83–99. 30 indexed citations
16.
Chang, Jyh‐Yeong, et al.. (2011). A functional-link based interval type-2 compensatory fuzzy neural network for nonlinear system modeling. 939–943. 11 indexed citations
17.
Juang, Chia‐Feng, et al.. (2010). A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing. Fuzzy Sets and Systems. 161(19). 2552–2568. 91 indexed citations
18.
Lin, Yang-Yin, Jyh‐Yeong Chang, & Chin‐Teng Lin. (2010). An Internal/Interconnection Recurrent Type-2 Fuzzy Neural Network (IRT2FNN) for dynamic system identification. 733–737. 2 indexed citations
19.
Juang, Chia‐Feng, Yang-Yin Lin, & I‐Fang Chung. (2009). Dynamic system identification using a Type-2 Recurrent Fuzzy Neural Network. Asian Control Conference. 768–772. 2 indexed citations
20.
Juang, Chia‐Feng, et al.. (2009). A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing. IEEE Transactions on Fuzzy Systems. 17(5). 1092–1105. 108 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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