Michiharu Maeda

722 total citations
67 papers, 476 citations indexed

About

Michiharu Maeda is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Michiharu Maeda has authored 67 papers receiving a total of 476 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Electrical and Electronic Engineering, 24 papers in Artificial Intelligence and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Michiharu Maeda's work include Neural Networks and Applications (16 papers), Advanced DC-DC Converters (11 papers) and Metaheuristic Optimization Algorithms Research (7 papers). Michiharu Maeda is often cited by papers focused on Neural Networks and Applications (16 papers), Advanced DC-DC Converters (11 papers) and Metaheuristic Optimization Algorithms Research (7 papers). Michiharu Maeda collaborates with scholars based in Japan, Germany and Zambia. Michiharu Maeda's co-authors include Hiromi Miyajima, O. Ichinokura, Shigeyuki Hamada, Mutsumi Matsumoto, Michikazu Nakai, Takaharu Tanaka, Hidekazu Sasaki, Takashi Ooshima, Andreas Ostermann and Toshiyuki Chatake and has published in prestigious journals such as Journal of Power Sources, Japanese Journal of Applied Physics and Neurocomputing.

In The Last Decade

Michiharu Maeda

50 papers receiving 399 citations

Peers

Michiharu Maeda
Comparison fields: 5 of 104
  • Electrical and Electronic Engineering 167
  • Artificial Intelligence 82
  • Electronic, Optical and Magnetic Materials 56
  • Molecular Biology 52
  • Materials Chemistry 44
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Citations per field, relative to Michiharu Maeda
Michiharu Maeda · 1×
Citations per year, relative to Michiharu Maeda
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Countries citing papers authored by Michiharu Maeda

Since Specialization
Citations

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

Fields of papers citing papers by Michiharu Maeda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michiharu Maeda. 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 Michiharu Maeda. The network helps show where Michiharu Maeda may publish in the future.

Co-authorship network of co-authors of Michiharu Maeda

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

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 0
3
Some Properties of Quantum Data Search Algorithms
7
4 1
5
Numerical Evaluation of Incremental Vector Quantization Using Stochastic Relaxation
4
6
State Sharing Methods in Statistical Fluctuation for Image Restoration
3
7
A Hybrid Learning Approach to Self-Organizing Neural Network for Vector Quantization
2
8 88
9
Adaptation Strength According to Neighborhood Ranking of Self-Organizing Neural Networks
1
10 3
11 4
12 3
13 0
14
Competitive Learning Methods with Refractory and Creative Approaches
2
15 13
16
Destructive Fuzzy Modeling Using Neural Gas Network
2
17
An Adaptive Learning and Self-Deleting Neural Network for Vector Quantization
5
18
Some Characteristics of Higher Order Neural Networks with Decreasing Energy Functions (Special Section on Nonlinear Theory and its Applications)
1
19 2
20 2

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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