P. Saratchandran
- Artificial Intelligence top 0.1%
- Control and Systems Engineering top 0.5%
- Electrical and Electronic Engineering top 5%
- Computer Vision and Pattern Recognition top 1%
- Signal Processing top 1%
- Co-authors
- Guang-Bin HuangN. SundararajanNanying LiangHai-Jun RongSuresh SundaramRunxuan ZhangAbhay A. PashilkarYan Li
- Topics
- Neural Networks and Applications (57 papers)Machine Learning and ELM (22 papers)Blind Source Separation Techniques (18 papers)
- Cited by
- Artificial IntelligenceControl and Systems EngineeringComputer Vision and Pattern Recognition
- Partner nations
- SingaporeIndiaUnited Kingdom
In The Last Decade
P. Saratchandran
87 papers receiving 5.8k citations
Hit Papers
Peers
Comparison fields: 5 of 148
- Artificial Intelligence 4.4k
- Control and Systems Engineering 1.6k
- Electrical and Electronic Engineering 1.2k
- Computer Vision and Pattern Recognition 933
- Signal Processing 476
Countries citing papers authored by P. Saratchandran
This map shows the geographic impact of P. Saratchandran'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 P. Saratchandran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites P. Saratchandran more than expected).
Fields of papers citing papers by P. Saratchandran
This network shows the impact of papers produced by P. Saratchandran. 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 P. Saratchandran. The network helps show where P. Saratchandran may publish in the future.
Co-authorship network of co-authors of P. Saratchandran
This figure shows the co-authorship network connecting the top 25 collaborators of P. Saratchandran. A scholar is included among the top collaborators of P. Saratchandran 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 P. Saratchandran. P. Saratchandran is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 277 | |
| 3 | A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networksbreakdown → | 1522 |
| 4 | 147 | |
| 5 | A Generalized Growing and Pruning RBF (GGAP-RBF) Neural Network for Function Approximationbreakdown → | 508 |
| 6 | 54 | |
| 7 | 303 | |
| 8 | 88 | |
| 9 | 0 | |
| 10 | 14 | |
| 11 | 6 | |
| 12 | 66 | |
| 13 | 4 | |
| 14 | 8 | |
| 15 | 5 | |
| 16 | 7 | |
| 17 | 5 | |
| 18 | Effect of hidden layers on generalization properties of feedforward neural networks. | 2 |
| 19 | Comments on "Dynamic Programming Approach to Optimal Weight Selection in Multilayer Neural Networks" | 1 |
| 20 | 1 |
About P. Saratchandran
P. Saratchandran is a scholar working on Artificial Intelligence, Signal Processing and Control and Systems Engineering, having authored 89 papers that have together received 6.1k indexed citations. Recurring topics across this work include Neural Networks and Applications (57 papers), Machine Learning and ELM (22 papers) and Blind Source Separation Techniques (18 papers). The work is most often cited by research in Artificial Intelligence (4.4k citations), Control and Systems Engineering (1.6k citations) and Computer Vision and Pattern Recognition (933 citations). P. Saratchandran has collaborated with scholars based in Singapore, India and United Kingdom. Frequent co-authors include Guang-Bin Huang, N. Sundararajan, N. Sundararajan, Nanying Liang, Hai-Jun Rong, Suresh Sundaram, Runxuan Zhang, Abhay A. Pashilkar, Yan Li and Qinyu Zhu. Their work appears in journals such as IEEE Transactions on Automatic Control, Automatica and Information Sciences.
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.