Emran Younesi
Papers
1
Total Citations
33
H-Index
1
About
Emran Younesi is a researcher whose work sits at the intersection of robotics, soft computing, and intelligent control systems. His primary research focuses on addressing the complex nonlinearities inherent in robotic manipulators, particularly in the modeling and forecasting of underactuated robotic systems. Younesi’s major contribution lies in applying advanced machine learning methodologies—such as support vector regression (SVR) with radial and polynomial basis functions—to estimate contact forces in robotic fingers, a task that traditional analytical modeling struggles to solve. His most cited paper, “Forecasting of Underactuated Robotic Finger Contact Forces by Support Vector Regression Methodology” (2016), has garnered 33 citations, demonstrating its relevance in the field of soft robotics and intelligent control. This work highlights his ability to bridge the gap between theoretical modeling and practical, data-driven estimation, offering a robust alternative for real-time robotic applications. Younesi’s research is particularly valuable for students and researchers exploring the integration of computational intelligence into robotic systems, as it provides a clear example of how soft computing can overcome the limitations of conventional analytical approaches in highly nonlinear environments.
Research Focus
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Top Papers
- 1