Fractional symbolic network entropy analysis for the fractional-order chaotic systems

Complexity analysis of fractional-order chaotic systems is an interesting topic of recent years. In this paper, the fractional symbolic network entropy measure algorithm is designed in which the symbol networks are built and fractional generalized information is introduced. Complexity of the fractional-order chaotic systems is analyzed. It shows that the proposed algorithm is effective for measure complexity of different pseudo random sequences. Complexity decreases with the decrease of derivative order in the fractional-order discrete chaotic system while changes with the derivative order in the fractional-order continuous chaotic system. Moreover, basin of attraction is also determined by the derivative order. It provides a basis for parameter choice of the fractional-order chaotic systems in the real applications. (paper)