Manman Yuan
Papers
1
Total Citations
48
H-Index
1
About
Manman Yuan is a prominent researcher in the field of nonlinear dynamics and control systems, with a particular focus on memristive neural networks and synchronization problems. Their major contributions lie in developing advanced sampled-data control strategies for complex neural network systems, addressing critical challenges such as leakage delays and additive time-varying delay components. Yuan's most-cited work, "Synchronization of memristive BAM neural networks with leakage delay and additive time-varying delay components via sampled-data control" (2017), has garnered 48 citations, reflecting its significance in the field. This paper introduced novel theoretical frameworks for stabilizing memristive bidirectional associative memory (BAM) neural networks, which are crucial for applications in pattern recognition, associative memory, and neuromorphic computing. Yuan's research has advanced the understanding of how sampled-data control can effectively manage the inherent complexities of memristive systems, offering practical solutions for real-time control in engineering applications. Their work continues to influence scholars working at the intersection of neural networks, control theory, and memristive technologies.
Research Focus
Key Achievements
Top Papers
- 1