Tianming Yuan
Papers
1
Total Citations
6
H-Index
1
About
Tianming Yuan is a researcher whose work lies at the intersection of robotics, neural networks, and computational optimization. His most cited paper, "A New Algorithm for Solving Inverse Kinematics of Robot Based on BP and RBF Neural Network" (2014), introduces a novel parallel neural network approach that combines Backpropagation (BP) and Radial Basis Function (RBF) networks to solve the complex inverse kinematics problem in robotics. By training the BP network with the Levenberg-Marquardt algorithm and leveraging the RBF network's radial basis functions, Yuan's method offers improved accuracy and efficiency over traditional techniques. This contribution has garnered 6 citations, reflecting its relevance to researchers tackling robotic motion planning and control. Yuan's work is particularly notable for its practical focus, providing concrete implementation steps and addressing key considerations for real-world application. His research bridges theoretical neural network design with tangible robotic challenges, making it a valuable resource for students and engineers seeking robust solutions to inverse kinematics. Through this work, Yuan demonstrates a commitment to advancing intelligent robotic systems, positioning himself as a thoughtful contributor to the fields of computational intelligence and automation.
Research Focus
Key Achievements
Top Papers
- 1