Advantages of $q$-logarithm representation over $q$-exponential representation from the sense of scale and shift on nonlinear systems

TL;DR

This paper compares $q$-logarithm and $q$-exponential representations in nonlinear systems, highlighting the advantages of $q$-logarithm for consistent scale and shift analysis, and introduces their applications in entropy and probability models.

Contribution

It introduces the $q$-logarithm representation as a superior tool for analyzing nonlinear systems with fixed scale units, compared to the traditional $q$-exponential approach.

Findings

$q$-logarithm offers better handling of scale and shift effects.

The paper derives entropy and probability expressions using $q$-logarithm.

$q$-logarithm provides theoretical advantages in nonlinear system analysis.

Abstract

Addition and subtraction of observed values can be computed under the obvious and implicit assumption that the scale unit of measurement should be the same for all arguments, which is valid even for any nonlinear systems. This paper starts with the distinction between exponential and non-exponential family in the sense of the scale unit of measurement. In the simplest nonlinear model ${dy}/{dx}=y^{q}$, it is shown how typical effects such as rescaling and shift emerge in the nonlinear systems and affect observed data. Based on the present results, the two representations, namely the $q$-exponential and the $q$-logarithm ones, are proposed. The former is for rescaling, the latter for unified understanding with a fixed scale unit. As applications of these representations, the corresponding entropy and the general probability expression for unified understanding with a fixed scale unit are presented. For the theoretical study of nonlinear systems, $q$-logarithm representation is shown to have significant advantages over $q$-exponential representation.