Logistic Population Modeling by Parameter Fitting and Gradient-Descent Techniques
Document Type
Conference Proceeding
Publication Date
4-2025
Abstract
This study examines the population growth of Mexico City since 1950. A logistic model is established to model the population in the city. The growth rate per capita and the carrying capacity of the city are set to be unknown parameters. We use part of the known population data to obtain these parameters by techniques of parameter fitting with Gradient-Descent and other optimization techniques. Computations are conducted by Matlab and Python coding language for different optimization approaches. Then the models are used to predict population of the city in recent years. The model is also analyzed for its predictability.
Recommended Citation
Munoz, Jorge and Castro, Angel, "Logistic Population Modeling by Parameter Fitting and Gradient-Descent Techniques" (2025). Student Research Symposium 2025. 8.
https://digitalcommons.tamusa.edu/srs_2025/8
Comments
1:00-2:00 p.m.
BLH 262
Studies in Mathematical, Physical & Engineering
Walter Den, Moderator