Prasenjit Mukherjee
Papers
1
Total Citations
2
H-Index
1
About
Prasenjit Mukherjee is a researcher whose work centers on the intersection of neural networks, fuzzy logic, and autonomous robotics. His primary contributions lie in developing optimized training methodologies for neural networks, specifically tailored for autonomous vehicle navigation in unknown environments. His most cited work, "Optimized Fuzzy Logic Training of Neural Networks for Autonomous Robotics Applications" (2011), addresses a critical bottleneck in robotics: the challenge of effectively training neural networks to handle real-world, unstructured scenarios. By integrating fuzzy logic into the training process, Mukherjee’s approach enhances the adaptability and decision-making capabilities of autonomous systems. Although his citation count is modest, his research tackles foundational problems in robotics and artificial intelligence, offering practical solutions for improving neural network performance in dynamic settings. His work is particularly relevant for students and researchers exploring hybrid AI systems, where the fusion of fuzzy logic and neural networks can lead to more robust and efficient autonomous agents.
Research Focus
Key Achievements
Top Papers
- 1