Explore MATLAB source code curated for "数学建模" with clean implementations, documentation, and examples.
Comprehensive compilation of China's National Undergraduate Mathematical Contest in Modeling competition problems along with award-winning solutions and analytical papers from multiple years
A comprehensive compilation of award-winning papers from the Graduate Mathematical Modeling Competition spanning 2004-2022, featuring advanced mathematical modeling techniques, algorithm implementations, and research methodologies suitable for technical study and reference.
Comprehensive archive of graduate-level mathematical modeling competition problems spanning 2004-2023, featuring real-world problem statements that require numerical analysis, algorithm design, and computational implementation using tools like MATLAB, Python, or R for mathematical modeling solutions.
Comprehensive collection of mathematical modeling course materials from Zhejiang University, covering fundamental concepts to advanced modeling techniques with practical implementation approaches
Performing nonlinear least squares fitting using MATLAB for mathematical modeling and experimentation, with algorithm selection and parameter optimization techniques
Teaching materials covering fundamental and advanced mathematical modeling concepts with practical algorithm implementations and code demonstration approaches
PPT courseware on the fundamentals of mathematical modeling, designed for beginners to build foundational knowledge and theoretical understanding.
Recent studies on mathematical modeling of visual cortical cells [Kulikowski/Marcelja/Bishop:1982] propose a tuned band-pass filter bank structure, characterized by Gaussian transfer functions in the frequency domain. Implementation typically involves Gabor filter approximations through inverse Fourier transforms of Gaussian functions modulated by complex sinusoids.
This study employs Newtonian dynamics methodology to establish a mathematical model of a single inverted pendulum system, followed by comparative simulation analysis of PID control (based on classical control theory), LQR control (based on optimal control theory), and fuzzy logic control methods. The simulation implementation includes system linearization techniques, state-space formulations for LQR design, and membership function configuration for fuzzy controllers. The analytical results provide theoretical guidance for research in this field, with practical insights into controller tuning parameters and stability margin comparisons.
A barbershop simulation system developed during our summer mathematical modeling project, featuring a comprehensive graphical user interface for operational analysis.