Aprilian Nur Wakhid Daini
Papers
1
Total Citations
2
H-Index
1
About
Aprilian Nur Wakhid Daini is a researcher focused on advancing intelligent control systems for autonomous robotics, particularly through neural network architectures. His work addresses critical challenges in data accuracy for backpropagation neural network-based controllers, a cornerstone for reliable autonomous navigation. His most-cited study, "Improvement of Data Accuracy on Backpropagation Neural Network-based Automatic Control System for Wheeled Robot" (2020), tackles the persistent problem of training data precision in direct inverse control schemes—a key approach for enabling robots to learn and execute complex maneuvers without explicit programming. By developing strategies to enhance data fidelity, Daini’s research directly improves the robustness and real-world applicability of neural network-driven control systems. While his citation count is still growing, his contributions are foundational for students and engineers working at the intersection of machine learning and robotics, offering practical solutions to improve autonomous system performance. Daini’s work exemplifies the iterative refinement needed to bridge the gap between theoretical neural network models and reliable, real-time robotic control.
Research Focus
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Top Papers
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