This project was completed for a client to optimize an ecological-economic model using advanced mathematical methods and tools. Fuzzy logic was integrated into the Leontief-Ford model through linear programming and data visualization techniques.
Key Tasks:
Implement optimization using fuzzy set theory.
Analyze the impact of fuzziness on decision-making processes.
Develop analytical tools for evaluating environmental strategies.
Methodology:
NumPy for numerical computations,
SciPy for linear programming,
matplotlib and mpl_toolkits.mplot3d for visualizing results.
Designed algorithms to handle multidimensional data and complex membership functions.
Tested algorithms on real-world ecological datasets.
Results:
Delivered efficient algorithms leveraging fuzzy logic.
Enhanced the robustness of models and their ability to account for uncertainty.
Produced clear visualizations to simplify data analysis.
Key Tasks:
Implement optimization using fuzzy set theory.
Analyze the impact of fuzziness on decision-making processes.
Develop analytical tools for evaluating environmental strategies.
Methodology:
NumPy for numerical computations,
SciPy for linear programming,
matplotlib and mpl_toolkits.mplot3d for visualizing results.
Designed algorithms to handle multidimensional data and complex membership functions.
Tested algorithms on real-world ecological datasets.
Results:
Delivered efficient algorithms leveraging fuzzy logic.
Enhanced the robustness of models and their ability to account for uncertainty.
Produced clear visualizations to simplify data analysis.