Xiang Chang
Papers
10
Total Citations
113
H-Index
7
About
Xiang Chang is a leading researcher at the intersection of robotics, neural network control, and intelligent automation, with a primary focus on robotic manipulation and calligraphy systems. Their most significant contributions lie in developing advanced neural network controllers for robotic grasping and nonlinear system control, as demonstrated by their highly cited work on "Visual-Guided Robotic Object Grasping Using Dual Neural Network Controllers" (33 citations), which simulates human hand-eye coordination for robust object reaching. Chang has pioneered the challenging field of robotic Chinese calligraphy, authoring multiple papers on stroke generation, trajectory learning, and writing sequence optimization, including "Automatic stroke generation for style-oriented robotic Chinese calligraphy" (15 citations) and "An LSTM Based Generative Adversarial Architecture for Robotic Calligraphy Learning System" (11 citations). Their innovative approach extends to model compression for neural network controllers (13 citations) and self-organizing type-2 fuzzy neural networks for uncertain systems (12 citations), addressing critical challenges in real-world robotic control. With over 100 total citations across ten publications from 2020-2024, Chang's work has established new paradigms for teaching robots complex, artistic motor skills while advancing fundamental control theory for nonlinear systems.
Research Focus
Key Achievements
Top Papers
- 1Visual-Guided Robotic Object Grasping Using Dual Neural Network Controllers33 citations · 2020
- 2Automatic stroke generation for style-oriented robotic Chinese calligraphy15 citations · 2021
- 3Model compression optimized neural network controller for nonlinear systems13 citations · 2023
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- 6Internal Model Control Structure Inspired Robotic Calligraphy System9 citations · 2023
- 7A Novel Self-Organizing Emotional CMAC Network for Robotic Control8 citations · 2020
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