Nonlinear System Identification Using Neural Network

@inproceedings{Arain2012NonlinearSI,
  title={Nonlinear System Identification Using Neural Network},
  author={Muhammad Asif Arain and Helon V. H. Ayala and Muhammad Adil Ansari},
  year={2012}
}
Magneto-rheological damper is a nonlinear system. In this case study, system has been identified using Neural Network tool. Optimization between number of neurons in the hidden layer and number of epochs has been achieved and discussed by using multilayer perceptron Neural Network. 
Highly Cited
This paper has 67 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 42 extracted citations

67 Citations

051015'11'13'15'17
Citations per Year
Semantic Scholar estimates that this publication has 67 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-7 of 7 references

A Hysteresis Model for the Field - dependent Damping Force of a Magnetorheological Damper

  • S. B. Choi, S. K. Lee
  • Journal on Sound and Vibration

An Adaptive Semiactive Control Algorithm for Magneto - rheological Suspension Systems

  • X. Song, M. Ahmadian, S. C. Southward, L. R. Miller
  • Journal of Vibration and Acoustics

Identification of Hammerstein systems without explicit parameterization of nonlinearity

  • J. Wang, A. Sano, T. Chen, B. Huang
  • International Journal of Control

Modeling Magnetorheological Dampers with Application of Nonparametric Approach

  • X. Song, M. Ahmadian, S. C. Southward
  • Journal of Intelligence Material Systems and…

Smart Passive System Based on MR Damper

  • S. W. Cho, H. J. Jung, J. H. Lee
  • JSSI 10 th Anniversary Symposium on Performance…

Similar Papers

Loading similar papers…