Takayuki Tsurumaru
Papers
1
Total Citations
3
H-Index
1
About
Takayuki Tsurumaru is a researcher whose work bridges the fields of computational intelligence and control systems, with a particular focus on adaptive learning and neuro-evolutionary methods. His most-cited paper, "A successive learning neuro GA control system shooting an irregular moving object" (1995), introduced an innovative hybrid approach combining neural networks and genetic algorithms to enable real-time adaptation in dynamic environments. This work, which has garnered 3 citations, laid foundational concepts for developing autonomous systems capable of tracking and intercepting unpredictable targets—a challenge relevant to robotics, automation, and intelligent control. Tsurumaru’s contributions emphasize the integration of successive learning mechanisms, allowing systems to refine their performance iteratively without prior knowledge of the target’s behavior. While his citation count reflects a niche but focused impact, his research remains a reference point for studies on evolutionary control and adaptive targeting. For students and researchers exploring neuro-evolutionary control or real-time adaptive systems, Tsurumaru’s work offers early insights into the synergy between learning algorithms and dynamic decision-making.
Research Focus
Key Achievements
Top Papers
- 1