Jiali Gao
Papers
1
Total Citations
3
H-Index
1
About
Jiali Gao is a pioneering researcher in computational intelligence and robotics, whose work bridges fractional calculus with neural network theory to solve complex real-time optimization problems. Their key research areas include recurrent neural networks, fractional-order dynamics, time-variant quadratic programming, and robot motion planning. Gao’s most significant contribution is the development of the Fractional-Order Zeroing Neural Network (FO-ZNN) model, which represents the first application of fractional calculus in neural models for robotic motion planning. This innovative approach diverges from standard ZNN architectures by leveraging fractional-order derivatives to achieve superior convergence and accuracy in solving time-variant quadratic programming problems. The work, published in 2024 with 3 citations, has opened new avenues for more efficient and robust robot control systems. Gao’s research demonstrates a unique ability to integrate advanced mathematical concepts with practical engineering applications, offering fresh perspectives on neural dynamics. Their contributions are particularly valuable for students and researchers interested in the intersection of fractional calculus, neural networks, and autonomous systems, providing a foundation for next-generation motion planning algorithms that can handle increasingly complex, time-sensitive robotic tasks.
Research Focus
Key Achievements
Top Papers
- 1