Falahudin Halim Shariski
Papers
1
Total Citations
3
H-Index
1
About
Falahudin Halim Shariski is a researcher in robotics and intelligent control systems, with a focus on applying unsupervised learning techniques to autonomous navigation. His most cited work, "Performance Analysis of Self-Organizing Map Method for Wheeled Robot Control System" (2020), introduces a novel approach by leveraging self-organizing maps (SOM)—an unsupervised learning method—as an alternative to traditional supervised backpropagation neural networks (BPNN) for wheeled robot control. This research demonstrates that SOM can effectively handle control tasks without requiring labeled training data, offering advantages in adaptability and simplicity. With 3 citations, the paper has contributed to the growing interest in unsupervised learning for robotics, particularly in environments where labeled data is scarce. Shariski’s work highlights the potential of SOM to reduce computational overhead while maintaining robust performance, making it a valuable reference for researchers exploring neural network-based control systems. His contributions are especially relevant for students and engineers seeking efficient, data-driven solutions for autonomous mobile robots.
Research Focus
Key Achievements
Top Papers
- 1