A. G. Ivakhnenko

2.8k total citations · 1 hit paper
23 papers, 1.9k citations indexed

About

A. G. Ivakhnenko is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Networks and Communications. According to data from OpenAlex, A. G. Ivakhnenko has authored 23 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 2 papers in Control and Systems Engineering and 1 paper in Computer Networks and Communications. Recurrent topics in A. G. Ivakhnenko's work include Statistical and Computational Modeling (15 papers), Neural Networks and Applications (4 papers) and Advanced Data Processing Techniques (2 papers). A. G. Ivakhnenko is often cited by papers focused on Statistical and Computational Modeling (15 papers), Neural Networks and Applications (4 papers) and Advanced Data Processing Techniques (2 papers). A. G. Ivakhnenko collaborates with scholars based in Ukraine, United States and Russia. A. G. Ivakhnenko's co-authors include Donald C. Wunsch, Igor V. Tetko, Tetiana Aksenova, Alessandro E. P. Villa, William J. Welsh, Walter L. Zielinski, Vasyl Kovalishyn and P.C. Parks and has published in prestigious journals such as Analytical Chemistry, IEEE Transactions on Automatic Control and Automatica.

In The Last Decade

A. G. Ivakhnenko

20 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Polynomial Theory of Complex Systems

1971 1.2k citations A. G. Ivakhnenko profile →

Peers

A. G. Ivakhnenko

AI

ME

EEE

CSE

EE

Timothy Masters United States

B.G. Horne United States

Eiji Mizutani Taiwan

Kishan G. Mehrotra United States

Vojislav Kecman New Zealand

Robert Hecht-Nielsen United States

Russell Reed United States

R.E. Uhrig United States

W.J. Melssen Netherlands

Michael A. Lehr United States

Timothy Masters United States

A. G. Ivakhnenko

670 ×0.7

AI

231 ×1.0

ME

206 ×0.9

EEE

175 ×0.8

CSE

242 ×1.3

EE

Citations per year, relative to A. G. Ivakhnenko A. G. Ivakhnenko (= 1×) peers Timothy Masters

Countries citing papers authored by A. G. Ivakhnenko

Since Specialization

Citations

This map shows the geographic impact of A. G. Ivakhnenko'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. Ivakhnenko 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. Ivakhnenko more than expected).

Fields of papers citing papers by A. G. Ivakhnenko

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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

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

All Works

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20 of 20 papers shown

1.

Ivakhnenko, A. G.. (2020). Past, Present, and Future of GMDH. 105–119. 1 indexed citations

2.

Ivakhnenko, A. G., et al.. (2008). Investigation of Efficiency of Additional Determination Method of the Model Selection in the Modeling Problems by Application of GMDH Algorithm. Journal of Automation and Information Sciences. 40(3). 47–58. 11 indexed citations

3.

Ivakhnenko, A. G., et al.. (2003). Inductive sorting-out GMDH algorithms with polynomial complexity for active neurons of neural network. 2. 1169–1173. 23 indexed citations

4.

Ivakhnenko, A. G., et al.. (2003). Determination of Regularities of Ions and Surface Interaction by Combinatorial GMDH Algorithm. Journal of Automation and Information Sciences. 35(4). 1–10. 1 indexed citations

5.

Ivakhnenko, A. G., et al.. (2002). GMDH Algorithm for Optimal Model Choice by the External Error Criterion with the Extension of Definition by Model Bias and Its Applications to the Committees and Neural Networks. 1 indexed citations

6.

Ivakhnenko, A. G., et al.. (2002). Problems of Further Development of GMDH Algorithms: Part 2. 7 indexed citations

7.

Ivakhnenko, A. G., et al.. (2002). Application of Algorithms of the Method of Batch Assessment of Arguments for Recovering Missed Data and Prediction of the Glucose Level in Blood on at Home Diabetes Monitoring. Journal of Automation and Information Sciences. 34(6). 8–8. 2 indexed citations

8.

Ivakhnenko, A. G., et al.. (2000). Algebraic Approach and Optimal Physical Clusterization in Interpolation Problems of Artificial Intelligence. 4 indexed citations

9.

Aksenova, Tetiana, Igor V. Tetko, A. G. Ivakhnenko, et al.. (1999). Pharmaceutical Fingerprinting in Phase Space. 1. Construction of Phase Fingerprints. Analytical Chemistry. 71(13). 2423–2430. 13 indexed citations

10.

Ivakhnenko, A. G., et al.. (1999). Application of Self-Organizing Neural Networks with Active Neurons for Prediction of Bioactivity of Chemical Compounds by the Analogues Search Algorithm. Journal of Automation and Information Sciences. 31(7-9). 51–58. 3 indexed citations

11.

Ivakhnenko, A. G., et al.. (1995). Self-organization of nets of active neurons. Systems Analysis Modelling Simulation. 20(1). 93–106. 27 indexed citations

12.

Ivakhnenko, A. G.. (1995). Self-organization of neuronet with active neurons for effects of nuclear tests explosions forecasting. Systems Analysis Modelling Simulation. 20(1). 107–116. 13 indexed citations

13.

Ivakhnenko, A. G., et al.. (1995). Present state and new problems of further GMDH development. Systems Analysis Modelling Simulation. 20(1). 3–16. 11 indexed citations

14.

Ivakhnenko, A. G.. (1979). Development of models of optimal complexity using self-organization theory. International Journal of Parallel Programming. 8(2). 111–127. 5 indexed citations

15.

Ivakhnenko, A. G.. (1976). Principles of neurodynamics. Cybernetics and Systems Analysis. 11(5). 841–842. 200 indexed citations

16.

Parks, P.C., et al.. (1975). A self-organizing model of the British economy for control with optimal prediction using the balance-of-variables criterion. International Journal of Parallel Programming. 4(4). 349–379. 5 indexed citations

17.

Ivakhnenko, A. G.. (1970). Heuristic self-organization in problems of engineering cybernetics. Automatica. 6(2). 207–219. 278 indexed citations

18.

Ivakhnenko, A. G.. (1967). CYBERNETIC SYSTEMS WITH COMBINED CONTROL. 4 indexed citations

19.

Ivakhnenko, A. G., et al.. (1966). CYBERNETIC PREDICTING DEVICES. 81 indexed citations

20.

Ivakhnenko, A. G.. (1963). Self-organizing systems with positive feedback loops. IEEE Transactions on Automatic Control. 8(3). 247–254. 1 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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