Intelligent Chaos Controller

A Computational Intelligence Based Approach for Data-Driven Real-World Systems
  • Jallu Krishnaiah
  • C. S. Kumar
  • M. A. Faruqi
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 132)


Recent developments have shown the possibility of constructing Bifurcation Diagrams for real-world chaotic system based on observed data. In the present work we demonstrate how the control can be achieved on the data-driven process/system based on the bifurcation diagram construction capability. In reality many physical and non-physical systems are very difficult to represent using a mathematical form; even if mathematical models exist, it would be a difficult task to build a controller which works in real-time. Moreover, if the considered system is chaotic in nature there are very few methods for are available controlling. On contrary there are large number of Chaos Control techniques when the considered system is/has a mathematical model. Based on the fundamental idea of these techniques, i.e. small perturbation at appropriate time is enough to control such a chaotic systems, the present method uses the global search capability of genetic algorithms to find a best perturbation to the control parameter at each step with a RNN model of the considered system as an objective function.


Chaotic System Bifurcation Diagram Model Predictive Control Recurrent Neural Network Chaotic Region 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jallu Krishnaiah
    • 1
  • C. S. Kumar
    • 2
  • M. A. Faruqi
    • 3
  1. 1.R&D, BHELTrichyIndia
  2. 2.Robotics and Intelligent Systems LabIIT KharagpurIndia
  3. 3.Azad Instistitute of Engineering and TechnologyLucknowIndia

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