Skip to main content
Article thumbnail
Location of Repository

Modelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Network models: Part 1—simulation studies

By Krishnaiah Jallu, S Kumar C and M Aslam Faruqi

Abstract

Many real-world processes tend to be chaotic and also do not lead to satisfactory analytical modelling. It has been shown here that for such chaotic processes represented through short chaotic noisy time-series, a multi-input and multi-output recurrent neural networks model can be built which is capable of capturing the process trends and predicting the future values from any given starting condition. It is further shown that this capability can be achieved by the Recurrent Neural Network model when it is trained to very low value of mean squared error. Such a model can then be used for constructing the Bifurcation Diagram of the process leading to determination of desirable operating conditions. Further, this multi-input and multi-output model makes the process accessible for control using open-loop/closed-loop approaches or bifurcation control etc. All these studies have been carried out using a low dimensional discrete chaotic system of Hénon Map as a representative of some real-world processes

Topics: Dynamical Systems, Machine Learning, Complexity Theory, Artificial Intelligence
Year: 2006
OAI identifier: oai:cogprints.org:4842

Suggested articles

Citations

  1. (1996). Applications of chaos and fractals in process systems engineering,
  2. (1993). Chaos in a class of satellite attitude maneuvers, Thesis,
  3. (1990). Controlling of chaos,
  4. (1981). Detecting strange attractors in turbulence: appeared in dynamical systems and turbulence,
  5. (2002). Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural networks,
  6. (2000). Dynamics of monthly rainfall-runoff process at the gota basin: A search for chaos,
  7. (1990). Experimental control of chaos,
  8. (1993). Method of controlling chaos in laser equations,
  9. (2003). Nonlinear system identification and model reduction using artificial neural networks,
  10. (1999). Smooth data modelling and stimulus-response via stabilisation of neural chaos,
  11. (2002). The control of higher dimension chaos: comparative results for the chaotic satellite attitude control[15]
  12. (1997). Using a neural network to calculate the sensitivity vectors in synchronisation of chaotic maps, in:

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.