A Relational Dimension Based Fractal Interpolation Algorithm for Chaotic Time Serials Modeling

This paper proposes a relational dimension based fractal interpolation algorithm for complex system chaotic time serials modeling, which resolves the disadvantage of the traditional random distribution model that the system space-time evolution rules can not be precisely described due to the unnecessary added degree of freedom. The Internet traffic is used as analysis object, the phase space is reconstructed and the system optimal delay is determined by auto correlation function. The system relational dimension is calculated out by saturation embedded dimension analysis and based on which the Hurst exponent of the system is gained. The chaotic time serials are reconstructed by using fractal Brown motion interpolation algorithm, which establishes a nonlinear model for precisely describing a chaotic system evolution process.