An improved differential evolution trained neural network scheme for nonlinear system identification

  • Bidyadhar Subudhi
  • Debashisha Jena


This paper presents an improved nonlinear system identification scheme using differential evolution (DE), neural network (NN) and Levenberg Marquardt algorithm (LM). With a view to achieve better convergence of NN weights optimization during the training, the DE and LM are used in a combined framework to train the NN. We present the convergence analysis of the DE and demonstrate the efficacy of the proposed improved system identification algorithm by exploiting the combined DE and LM training of the NN and suitably implementing it together with other system identification methods, namely NN and DE+NN on a number of examples including a practical case study. The identification results obtained through a series of simulation studies of these methods on different nonlinear systems demonstrate that the proposed DE and LM trained NN approach to nonlinear system identification can yield better identification results in terms of time of convergence and less identification error.


Differential evolution neural network (NN) nonlinear system identification Levenberg Marquardt algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    S. Chen, S. A. Billings, W. Luo. Orthogonal Least Squares Methods and Their Application to Non-linear System Identification. International Journal of Control, vol. 50, no. 5, pp. 1873–1896, 1989.zbMATHCrossRefMathSciNetGoogle Scholar
  2. [2]
    S. A. Billings, H. L. Wei. A New Class of Wavelet Networks for Nonlinear System Identification. IEEE Transactions on Neural Networks, vol. 16, no. 4, pp. 862–874, 2005.CrossRefGoogle Scholar
  3. [3]
    K. S. Narendra, K. Parthaasarathy. Identification and Control of Dynamical Systems Using Neural Networks. IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 4–27, 1990.CrossRefGoogle Scholar
  4. [4]
    R. Storn. System Design by Constraint Adaptation and Differential Evolution. IEEE Transactions on Evolutionary Computation, vol. 3, no. 1, pp. 22–34, 1999.CrossRefGoogle Scholar
  5. [5]
    J. Ilonen, J. K. Kamarainen, J. Lampinen. Differential Evolution Training Algorithm for Feed Forward Neural Networks. Neural Processing Letters, vol. 17, no. 1, pp. 93–105, 2003.CrossRefGoogle Scholar
  6. [6]
    O. Ludwig Jr., P. C. Gonzalez, A. C. de C. Lima. Optimization of ANN Applied to Non-linear System Identification Based in UWB. In Proceedings of the Symposium on Trends in Communications, IEEE Press, Slovakia, pp. 56–59, 2006.Google Scholar
  7. [7]
    C. T. Lin, C. S. G. Lee. Neural Fuzzy Systems: A Neurofuzzy Synergism to Intelligent Systems, Prentice-Hall, Inc., New Jersey, USA, 1996.Google Scholar
  8. [8]
    G. E.P. Box, G. M. Jenkins. Time Series Analysis, Forecasting and Control, Holden Day, San Francisco, USA, 1970.zbMATHGoogle Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH 2009

Authors and Affiliations

  1. 1.Center for Industrial Electronics & Robotics, Department of Electrical EngineeringNational Institute of TechnologyRourkelaIndia

Personalised recommendations