Improved numerical solutions for chaotic-cancer-model

Abstract

In biological sciences, dynamical system of cancer model is well known due to its sensitivity and chaoticity. Present work provides detailed computational study of cancer model by counterbalancing its sensitive dependency on initial conditions and parameter values. Cancer chaotic model is discretized into a system of nonlinear equations that are solved using the well-known Successive-Over-Relaxation (SOR) method with a proven convergence. This technique enables to solve large systems and provides more accurate approximation which is illustrated through tables, time history maps and phase portraits with detailed analysis.

Chaos in a Cancer Model via Fractional Derivatives with Exponential Decay and Mittag-Leffler Law

“…The other fractional order chaotic systems were described in many other works [38][39][40][41]. In cancer models, the dynamics of the interactions of the tumour cells with other cells may exhibit chaos [42][43][44][45].…”