Reconstruction mechanism with self-growing equations for hyper, improper and proper fractional chaotic systems through a novel non-Lyapunov approach

Abstract

Identification of the unknown parameters and orders of fractional chaotic systems is of vital significance in controlling and synchronization of fractional-order chaotic systems. However there exist basic hypotheses in traditional estimation methods, that is, the parameters and fractional orders are partially known or the known data series coincide with definite forms of fractional chaotic differential equations except some uncertain parameters and fractional orders. What should I do when these hypotheses do not exist? In this paper, a non-Lyapunov novel approach with a novel united mathematical model is proposed to reconstruct fractional chaotic systems, through the fractional-order differential equations self-growing mechanism by some genetic operations ideas independent of these hypotheses. And the cases of identifying the unknown parameters and fractional orders of fractional chaotic systems can be thought as special cases of the proposed united mathematical reconstruction method in non-Lyapunov way. The problems of fractional-order chaos reconstruction are converted into a multiple modal non-negative special functions' minimization through a proper translation, which takes fractional-order differential equations as its particular independent variables instead of the unknown parameters and fractional orders. And the objective is to find best form of fractional-order differential equations such that the objective function is minimized. Simulations are done to reconstruct a series of hyper and normal fractional chaotic systems. The experiments' results show that the proposed self-growing mechanism of fractional-order differential equations with genetic operations is a successful methods for fractional-order chaotic systems' reconstruction, with the advantages of high precision and robustness.