Su Lee Goh

768 total citations
19 papers, 535 citations indexed

About

Su Lee Goh is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Su Lee Goh has authored 19 papers receiving a total of 535 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 11 papers in Signal Processing and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Su Lee Goh's work include Neural Networks and Applications (16 papers), Blind Source Separation Techniques (9 papers) and Target Tracking and Data Fusion in Sensor Networks (8 papers). Su Lee Goh is often cited by papers focused on Neural Networks and Applications (16 papers), Blind Source Separation Techniques (9 papers) and Target Tracking and Data Fusion in Sensor Networks (8 papers). Su Lee Goh collaborates with scholars based in United Kingdom, Japan and Germany. Su Lee Goh's co-authors include Danilo P. Mandic, Kazuyuki Aihara, Dobrivoje Popović, Dragan Obradović, Anthony Kuh, Phebe Vayanos, Temujin Gautama, Tomasz M. Rutkowski, Beth Jelfs and Mo Chen and has published in prestigious journals such as IEEE Transactions on Signal Processing, Renewable Energy and Neural Computation.

In The Last Decade

Su Lee Goh

18 papers receiving 519 citations

Peers

Su Lee Goh
Vanessa Su Lee Goh United Kingdom
Jiuchao Feng China
Xiao-Hu Yu China
Tirza Routtenberg Israel
Sayed Pouria Talebi United Kingdom
Jesús Navarro-Moreno Spain
Marci Hoffman United States
Dimitris Tzikas Greece
M. Novey United States
Pedro A. Forero United States
Vanessa Su Lee Goh United Kingdom
Su Lee Goh
Citations per year, relative to Su Lee Goh Su Lee Goh (= 1×) peers Vanessa Su Lee Goh

Countries citing papers authored by Su Lee Goh

Since Specialization
Citations

This map shows the geographic impact of Su Lee Goh'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 Su Lee Goh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Su Lee Goh more than expected).

Fields of papers citing papers by Su Lee Goh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Su Lee Goh. 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 Su Lee Goh. The network helps show where Su Lee Goh may publish in the future.

Co-authorship network of co-authors of Su Lee Goh

This figure shows the co-authorship network connecting the top 25 collaborators of Su Lee Goh. A scholar is included among the top collaborators of Su Lee Goh 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 Su Lee Goh. Su Lee Goh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Mandic, Danilo P., et al.. (2008). Complex-valued prediction of wind profile using augmented complex statistics. Renewable Energy. 34(1). 196–201. 99 indexed citations
2.
Mandic, Danilo P., Phebe Vayanos, Mo Chen, & Su Lee Goh. (2008). ONLINE DETECTION OF THE MODALITY OF COMPLEX-VALUED REAL WORLD SIGNALS. International Journal of Neural Systems. 18(2). 67–74. 8 indexed citations
3.
Goh, Su Lee & Danilo P. Mandic. (2007). An Augmented Extended Kalman Filter Algorithm for Complex-Valued Recurrent Neural Networks. Neural Computation. 19(4). 1039–1055. 85 indexed citations
4.
Goh, Su Lee & Danilo P. Mandic. (2007). An augmented CRTRL for complex-valued recurrent neural networks. Neural Networks. 20(10). 1061–1066. 31 indexed citations
5.
Mandic, Danilo P., Phebe Vayanos, Beth Jelfs, et al.. (2007). Collaborative Adaptive Learning using Hybrid Filters. III–921. 33 indexed citations
6.
Goh, Su Lee & Danilo P. Mandic. (2006). An Augmented Extended Kalman Filter Algorithm for Complex-Valued Recurrent Neural Networks. 5. V–561. 6 indexed citations
7.
Goh, Su Lee & Danilo P. Mandic. (2006). A Class of Gradient-Adaptive Step Size Algorithms for Complex-Valued Nonlinear Neural Adaptive Filters. 5. 253–256. 6 indexed citations
8.
Vayanos, Phebe, Su Lee Goh, & Danilo P. Mandic. (2006). Online Detection of the Nature of Complex-Valued Signals. 63. 173–178. 3 indexed citations
9.
Mandic, Danilo P., Su Lee Goh, & Kazuyuki Aihara. (2006). Sequential Data Fusion via Vector Spaces: Complex Modular Neural Network Approach. 147–151. 8 indexed citations
10.
Mandic, Danilo P., Su Lee Goh, & Kazuyuki Aihara. (2006). Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain. The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology. 48(1-2). 99–108. 11 indexed citations
11.
Obradović, Dragan, et al.. (2005). Multi-step forecasting using echo state networks. 159. 1574–1577. 6 indexed citations
12.
Goh, Su Lee, et al.. (2005). Complex-valued forecasting of wind profile. Renewable Energy. 31(11). 1733–1750. 109 indexed citations
13.
Goh, Su Lee & Danilo P. Mandic. (2005). A class of low complexity and fast converging algorithms for complex-valued neural networks. 2. 13–22. 1 indexed citations
14.
Goh, Su Lee & Danilo P. Mandic. (2005). Nonlinear adaptive prediction of complex-valued signals by complex-valued PRNN. IEEE Transactions on Signal Processing. 53(5). 1827–1836. 33 indexed citations
15.
Goh, Su Lee, Dobrivoje Popović, & Danilo P. Mandic. (2004). Complex-valued estimation of wind profile and wind power. 1037–1040 Vol.3. 11 indexed citations
16.
Goh, Su Lee & Danilo P. Mandic. (2004). A Complex-Valued RTRL Algorithm for Recurrent Neural Networks. Neural Computation. 16(12). 2699–2713. 60 indexed citations
17.
Goh, Su Lee, Zdenka Babić, & Danilo P. Mandic. (2004). An adaptive amplitude learning algorithm for nonlinear adaptive IIR filters. 1. 313–316. 1 indexed citations
18.
Goh, Su Lee & Danilo P. Mandic. (2003). Recurrent neural networks with trainable amplitude of activation functions. Neural Networks. 16(8). 1095–1100. 24 indexed citations
19.

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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