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Parameter identification via neural networks with fast convergence

Author

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  • Yadaiah, N.
  • Sivakumar, L.
  • Deekshatulu, B.L.

Abstract

The parameter identification using artificial neural networks is becoming very popular. In this chapter, the parameters of dynamical system are identified using artificial neural networks. A fast gradient decent technique for the parameter identification of a linear dynamical system has been presented. The following concepts are used for training of neural networks while identifying the system parameters: (1) batch wise training of neural networks; (2) variable learning parameter and; (3) an intelligent check over the rate at which parameters are converging. The complete algorithm is summarized as a flow chart. A detailed mathematical formulation is given. The simulation results and a comparative study with existing method is included.

Suggested Citation

  • Yadaiah, N. & Sivakumar, L. & Deekshatulu, B.L., 2000. "Parameter identification via neural networks with fast convergence," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 51(3), pages 157-167.
    • Handle: RePEc:eee:matcom:v:51:y:2000:i:3:p:157-167
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    Cited by:

    1. Sun, Mei & Wang, Xiaofang & Chen, Ying & Tian, Lixin, 2011. "Energy resources demand-supply system analysis and empirical research based on non-linear approach," Energy, Elsevier, vol. 36(9), pages 5460-5465.
    2. Han, Lili & Wu, Fangxiang & Sheng, Jie & Ding, Feng, 2012. "Two recursive least squares parameter estimation algorithms for multirate multiple-input systems by using the auxiliary model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(5), pages 777-789.

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