International Journal of Fuzzy Systems

, Volume 19, Issue 2, pp 452–469 | Cite as

FCPN Approach for Uncertain Nonlinear Dynamical System with Unknown Disturbance

  • Vandana Sakhre
  • Uday Pratap Singh
  • Sanjeev Jain


In this work, we have used a fuzzy counter-propagation network (FCPN) model to control different discrete-time, uncertain nonlinear dynamic systems with unknown disturbances. Fuzzy competitive learning (FCL) is used to process the weight connection and make adjustments between the instar and the outstar of the network. FCL paradigm adopts the principle of learning, used for calculation of the Best Matched Node (BMN) in the instar–outstar network. FCL provides a control of discrete-time uncertain nonlinear dynamic systems having dead zone and backlash. The errors like mean absolute error (MAE), mean square error (MSE), and best fit rate, etc. of FCPN are compared with networks like dynamic network (DN) and back propagation network (BPN). The FCL foretells that the proposed FCPN method gives better results than DN and BPN. The success and enactments of the proposed FCPN are validated through simulations on different discrete-time uncertain nonlinear dynamic systems and Mackey–Glass univariate time series data with unknown disturbances over BPN and DN.


Fuzzy counter-propagation network Back propagation network Dynamic network Unknown disturbances Uncertain nonlinear discrete time system Mackey–Glass time series data 



The author is gratefully acknowledged the financial assistance provided by the All India Council of Technical Education (AICTE) in the form of Research Promotion Scheme (RPS) project in 2012.


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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Vandana Sakhre
    • 1
  • Uday Pratap Singh
    • 1
  • Sanjeev Jain
    • 1
  1. 1.Madhav Institute of Technology & ScienceGwaliorIndia

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