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Related Experiment Videos

Nonlinear system identification using a Gabor/Hopfield network.

C Q Zhang1, M Sami Fadali

  • 1Dept. of Electr. Eng., Nevada Univ., Reno, NV.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 1, 1996
PubMed
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A novel Gabor/Hopfield network enhances nonlinear system identification for discrete-time models. This method improves upon previous techniques, offering robust identification for bounded-input-bounded-output stable systems.

Area of Science:

  • Control Systems Engineering
  • Computational Intelligence
  • Nonlinear System Dynamics

Background:

  • Traditional nonlinear system identification methods often struggle with complex dynamics and stability constraints.
  • Previous approaches using Gabor basis functions (GBF) with back-propagation neural networks faced limitations, including local minima.
  • Accurate modeling of discrete-time nonlinear systems is crucial for effective control and analysis.

Purpose of the Study:

  • To introduce a new Gabor/Hopfield network for nonlinear system identification.
  • To identify nonlinear discrete-time models that are affine linear in the control.
  • To improve upon existing Gabor basis function network performance.

Main Methods:

  • Development and application of a novel Gabor/Hopfield network architecture.

Related Experiment Videos

  • Derivation of Gabor model properties and global error minimum achievement guidelines.
  • Computer simulations to investigate network performance and practical considerations.

    • Main Results:

    • The Gabor/Hopfield network successfully identifies nonlinear discrete-time models affine linear in control.
    • The network demonstrates improved performance over prior Gabor basis function methods.
    • Analysis of practical issues including noise sensitivity, local minima, and training data selection was performed.

    Conclusions:

    • The proposed Gabor/Hopfield network offers a significant advancement in nonlinear system identification.
    • The method is effective for bounded-input-bounded-output (BIBO) stable systems, allowing identification over a large input-output range.
    • Further research can explore optimizing training strategies and network parameters for enhanced robustness.