Parameter Estimation of Chaotic Systems Using Fireworks Algorithm

  • Hao LiEmail author
  • Peng Bai
  • Jun-Jie Xue
  • Jie Zhu
  • Hui Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9141)


Chaotic system is a nonlinear deterministic system, and parameter identification for the chaotic system is an important issue in nonlinear science, such as secure communication, etc. By setting up an appropriate objective function, the parameter identification can be converted into a multi-dimensional optimization problem which can be solved by evolutionary algorithms. Emerging as an evolutionary algorithm, Fireworks Algorithm (FWA) has shown its good computational performance and robustness. In order to expand the application of FWA, several types of FWA are applied to estimate the parameters for two typical chaotic systems in which three parameters are totally unknown, simulation results show most of FWAs can have better estimation precision and robustness, and FWA is a new effective parameter identification method for the chaotic systems.


Particle Swarm Optimization Chaotic System Swarm Intelligence Lorenz System Swarm Intelligence Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hao Li
    • 1
    • 2
    Email author
  • Peng Bai
    • 1
  • Jun-Jie Xue
    • 1
  • Jie Zhu
    • 1
  • Hui Zhang
    • 2
  1. 1.Air Force Engineering UniversityXi’anChina
  2. 2.Department of IntelligenceAir Force Early-Warning AcademyWuhanChina

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