Identification of unknown parameters and orders via cuckoo search   oriented statistically by differential evolution for non-commensurate   fractional order chaotic systems
  Identification of the unknown parameters and orders of fractional chaotic systems is of vital significance in controlling and synchronization of fractional-order chaotic systems. In this paper, a non-Lyapunov novel approach is proposed to estimate the unknown parameters and orders together for non-commensurate and hyper fractional chaotic systems based on cuckoo search oriented statistically the differential evolution (CSODE). Firstly, a novel Gao's mathematical model is put and analysed in three sub-models, not only for the unknown orders and parameters' identification but also for systems' reconstruction of fractional chaos systems with time-delays or not. Then the problems of fractional-order chaos' identification are converted into a multiple modal non-negative functions' minimization through a proper translation, which takes fractional-orders and parameters as its particular independent variables. And the objective is to find best combinations of fractional-orders and systematic parameters of fractional order chaotic systems as special independent variables such that the objective function is minimized. Simulations are done to estimate a series of non-commensurate and hyper fractional chaotic systems with the new approaches based on CSODE, the cuckoo search and differential evolution respectively. The experiments' results show that the proposed identification mechanism based on CSODE for fractional-orders and parameters is a successful methods for fractional-order chaotic systems, with the advantages of high precision and robustness. 
Fei Gao, Feng-xia Fei, Qian Xu, Yan-fang Deng, Yi-bo Qi
nlin.CD math-ph math.MP