MATHEMATICAL MODEL FOR SCHIZOPHRENIA: PARAMETER ESTIMATION

Abstract

In this study, a fractional-order time-delay mathematical model for schizophrenia is investigated with a focus on parameter estimation. The model incorporates memory effects and delayed neural responses using Caputo fractional derivatives. A least squares approximation technique is employed to estimate unknown model parameters based on available data. Furthermore, the Fisher Information Matrix and profile likelihood methods are used to analyze parameter sensitivity and uncertainty. Numerical simulations are performed using MATLAB to validate the proposed approach. The results demonstrate that the fractional-order framework provides improved modeling accuracy and better representation of schizophrenia-related brain dynamics compared to classical integer-order models.