Classification of Uncertainties in Modeling of Complex Biological Systems

Abstract

An analogue of the Heisenberg uncertainty principle (complexity) is introduced for complex biological systems in the framework of the new chaos-self-organization theory. The requirement for such an analysis is determined by the absence of stationary regimes of biosystems (dx/dt ≠ 0 is continuous for the state vector complexity x(t)), uninterrupted chaotic change of the statistical functions f (x) and other parameters. At present, this is defined as type 2 uncertainty. At the same time, type 1 uncertainty is introduced for the complexity when f (x) do not change, and the quasi-attractor parameters can change. A neuron network simulator finds the differences between the samples in the absence of statistical differences.