"Logistic Population Modeling by Parameter Fitting and Gradient-Descent" by Jorge Munoz and Angel Castro
 

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.

Comments

1:00-2:00 p.m.

BLH 262

Studies in Mathematical, Physical & Engineering

Walter Den, Moderator

Share

COinS