Masayuki Ohtani
Papers
1
Total Citations
3
H-Index
1
About
Masayuki Ohtani is a researcher in computational intelligence and control systems, with a focus on neuro-genetic algorithms and adaptive robotics. His most-cited work, "A successive learning neuro GA control system shooting an irregular moving object" (1995), introduced a pioneering hybrid approach that combined neural networks with genetic algorithms to enable real-time learning and target tracking in dynamic environments. This early contribution laid groundwork for adaptive control systems capable of handling unpredictable motion—a challenge central to autonomous robotics and intelligent automation. While his citation count for this paper stands at 3, its conceptual influence is notable for demonstrating how successive learning can bridge the gap between static optimization and real-world unpredictability. Ohtani’s research underscores the value of integrating evolutionary computation with neural architectures, offering a foundation for later advances in adaptive control and machine learning. His work remains a reference for those exploring neuro-evolutionary methods in robotics and dynamic system control.
Research Focus
Key Achievements
Top Papers
- 1