A.G. Parlos

3.0k total citations
96 papers, 2.2k citations indexed

About

A.G. Parlos is a scholar working on Control and Systems Engineering, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, A.G. Parlos has authored 96 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Control and Systems Engineering, 30 papers in Artificial Intelligence and 18 papers in Aerospace Engineering. Recurrent topics in A.G. Parlos's work include Fault Detection and Control Systems (43 papers), Neural Networks and Applications (26 papers) and Machine Fault Diagnosis Techniques (23 papers). A.G. Parlos is often cited by papers focused on Fault Detection and Control Systems (43 papers), Neural Networks and Applications (26 papers) and Machine Fault Diagnosis Techniques (23 papers). A.G. Parlos collaborates with scholars based in United States, South Korea and Egypt. A.G. Parlos's co-authors include Amir F. Atiya, Kyusung Kim, Hamid A. Toliyat, Kil To Chong, M.S. Arefeen, Raj Bharadwaj, W.K. Tsai, B. Fernandez, S. Nandi and Nikolaos Kazantzis and has published in prestigious journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Industrial Electronics and IEEE Transactions on Signal Processing.

In The Last Decade

A.G. Parlos

89 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
A.G. Parlos United States	25	1.2k	726	674	368	191	96	2.2k
D.P. Atherton United Kingdom	30	3.5k 2.8×	444 0.6×	700 1.0×	408 1.1×	252 1.3×	213	4.3k
M.J. Grimble United Kingdom	34	3.8k 3.1×	651 0.9×	320 0.5×	527 1.4×	425 2.2×	406	4.7k
Dave Powell United States	6	1.4k 1.2×	170 0.2×	632 0.9×	458 1.2×	237 1.2×	12	2.5k
Maria Gabriella Xibilia Italy	25	1.2k 1.0×	832 1.1×	333 0.5×	321 0.9×	91 0.5×	149	2.6k
M. L. Workman United States	6	1.6k 1.3×	175 0.2×	639 0.9×	508 1.4×	242 1.3×	6	2.6k
Peng Li China	29	1.2k 1.0×	542 0.7×	1.1k 1.6×	315 0.9×	229 1.2×	162	3.0k
Qiang Zhou China	28	652 0.5×	230 0.3×	1.1k 1.7×	379 1.0×	70 0.4×	203	2.7k
Shiyou Yang China	23	671 0.5×	362 0.5×	1.3k 1.9×	333 0.9×	195 1.0×	173	2.1k
Ali Khaki Sedigh Iran	23	1.6k 1.3×	407 0.6×	304 0.5×	234 0.6×	153 0.8×	240	2.4k
Tyrone L. Vincent United States	26	1.1k 0.9×	150 0.2×	1.2k 1.8×	153 0.4×	150 0.8×	134	2.5k

Countries citing papers authored by A.G. Parlos

Since Specialization

Citations

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

Fields of papers citing papers by A.G. Parlos

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A.G. Parlos. 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 A.G. Parlos. The network helps show where A.G. Parlos may publish in the future.

Co-authorship network of co-authors of A.G. Parlos

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Zhang, Hongchao, Kil To Chong, Nikolaos Kazantzis, & A.G. Parlos. (2012). Discretization of nonlinear input-driven dynamical systems using the Adomian Decomposition Method. Applied Mathematical Modelling. 36(12). 5856–5875. 4 indexed citations

Atiya, Amir F., et al.. (2012). Parameter Estimation of 2-DOF System Based on Unscented Kalman Filter. Journal of the Korean Society for Precision Engineering. 29(10). 1128–1136. 1 indexed citations

Kim, Tae-Young, et al.. (2011). 파티클 필터를 이용한 비선형 시스템의 파라미터 추정에 관한 연구. 69–70.

Parlos, A.G., et al.. (2007). Predictive Path Switching Control for Improving the Quality of Service in Real-Time Applications. IEEE Journal of Selected Topics in Signal Processing. 1(2). 308–318. 4 indexed citations

Atiya, Amir F., Mohammed A. M. Aly, & A.G. Parlos. (2005). Sparse Basis Selection: New Results and Application to Adaptive Prediction of Video Source Traffic. IEEE Transactions on Neural Networks. 16(5). 1136–1146. 28 indexed citations

Parlos, A.G., et al.. (2003). Adaptive State Filtering for Space Shuttle Main Engine Turbine Health Monitoring. Journal of Spacecraft and Rockets. 40(1). 101–109. 4 indexed citations

Parlos, A.G. & Kyu‐Sung Kim. (2003). Model-based incipient fault diagnosis - multi-step neuro-predictors and multiresolution signal processing. 3. 317–322. 5 indexed citations

Parlos, A.G., et al.. (2001). An algorithmic approach to adaptive state filtering using recurrent neural networks. IEEE Transactions on Neural Networks. 12(6). 1411–1432. 80 indexed citations

Atiya, Amir F. & A.G. Parlos. (2000). New results on recurrent network training: unifying the algorithms and accelerating convergence. IEEE Transactions on Neural Networks. 11(3). 697–709. 357 indexed citations

10.

Parlos, A.G., et al.. (2000). Multi-step-ahead prediction using dynamic recurrent neural networks. Neural Networks. 13(7). 765–786. 127 indexed citations

11.

Morgan, James, et al.. (1997). Gain-Scheduled Adaptive Control of a Hybrid Structure. OakTrust (Texas A&M University Libraries). 260–261. 4 indexed citations

12.

Atiya, Amir F. & A.G. Parlos. (1995). Identification of nonlinear dynamics using a general spatio-temporal network. Mathematical and Computer Modelling. 21(1-2). 53–71. 6 indexed citations

13.

Parlos, A.G., et al.. (1994). An accelerated learning algorithm for multilayer perceptron networks. IEEE Transactions on Neural Networks. 5(3). 493–497. 72 indexed citations

14.

Parlos, A.G., Kil To Chong, & Amir F. Atiya. (1994). Application of the recurrent multilayer perceptron in modeling complex process dynamics. IEEE Transactions on Neural Networks. 5(2). 255–266. 151 indexed citations

15.

Parlos, A.G., et al.. (1994). Incipient Fault Detection and Identification in Process Systems Using Accelerated Neural Network Learning. Nuclear Technology. 105(2). 145–161. 32 indexed citations

16.

Parlos, A.G., M. Jayakumar, & Amir F. Atiya. (1992). Early detection of incipient faults in power plants using accelerated neural network learning. Transactions of the American Nuclear Society. 66. 3 indexed citations

17.

Parlos, A.G., Amir F. Atiya, & Kil To Chong. (1991). Dynamic gradient descent learning algorithms for enhanced empirical modeling of power plants. Transactions of the American Nuclear Society. 64(1). 1–8. 1 indexed citations

18.

Parlos, A.G., et al.. (1990). Gain-scheduled nonlinear control of u-tube steam generators at low powers. OakTrust (Texas A&M University Libraries). 63. 1 indexed citations

19.

Jayasuriya, Suhada, et al.. (1990). Active rejection of persistent disturbances in flexible space structures. 2 indexed citations

20.

Abed, Eyad H., et al.. (1990). On bifurcations in power system models and voltage collapse. 3014–3015 vol.6. 21 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.

Explore authors with similar magnitude of impact