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.
Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy
2012357 citationsMd Abdus Samad Kamal, Masakazu Mukai et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Junichi Murata
Since
Specialization
Citations
This map shows the geographic impact of Junichi Murata'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 Junichi Murata with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junichi Murata more than expected).
This network shows the impact of papers produced by Junichi Murata. 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 Junichi Murata. The network helps show where Junichi Murata may publish in the future.
Co-authorship network of co-authors of Junichi Murata
This figure shows the co-authorship network connecting the top 25 collaborators of Junichi Murata.
A scholar is included among the top collaborators of Junichi Murata 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 Junichi Murata. Junichi Murata is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Takano, Hirotaka, et al.. (2013). A study on visual abstraction for reinforcement learning problem using Learning Vector Quantization. Society of Instrument and Control Engineers of Japan. 1326–1331.2 indexed citations
3.
Takano, Hirotaka, et al.. (2013). Re-labeling Differential Evolution for combinatorial optimization. Society of Instrument and Control Engineers of Japan. 1550–1555.1 indexed citations
4.
Murata, Junichi, et al.. (2011). A study on use of prior information for acceleration of reinforcement learning. Society of Instrument and Control Engineers of Japan. 537–543.3 indexed citations
5.
Takano, Hirotaka, et al.. (2011). Daily solar radiation prediction based on wavelet analysis. Society of Instrument and Control Engineers of Japan. 712–717.26 indexed citations
6.
Kamal, Md Abdus Samad, Masakazu Mukai, Junichi Murata, & Tohru Kawabe. (2009). Development of ecological driving assist system model predictive approach in vehicle control.2 indexed citations
7.
Murata, Junichi, et al.. (2007). Introduction and control of subgoals in reinforcement learning. 329–334.2 indexed citations
Hirasawa, Kotaro, et al.. (2002). Increasing robustness of genetic algorithm. Genetic and Evolutionary Computation Conference. 456–462.
12.
Hirasawa, Kotaro, et al.. (2002). A new model to realize variable size Genetic Network Programming. Genetic and Evolutionary Computation Conference. 890–890.1 indexed citations
13.
Hirasawa, Kotaro, et al.. (2001). Genetic symbiosis algorithm for multiobjective optimization problems. Genetic and Evolutionary Computation Conference. 771–771.4 indexed citations
14.
Hirasawa, Kotaro, et al.. (2001). Network structure oriented evolutionary model–genetic network programming–and its Comparison with genetic programming. Genetic and Evolutionary Computation Conference. 38(5). 179–179.40 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.