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A robust nonlinear identification algorithm using PRESS statistic and forward regression.

X Hong1, P M Sharkey, K Warwick

  • 1Dept. of Cybern., Univ. of Reading, UK.

IEEE Transactions on Neural Networks
|February 2, 2008
PubMed
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This study presents a robust nonlinear identification algorithm using predicted residual sums of squares (PRESS) and forward regression. The method automates model evaluation without needing separate validation data, ensuring good generalization.

Area of Science:

  • Engineering
  • Systems Science

Background:

  • Nonlinear system identification is crucial for understanding complex dynamics.
  • Traditional methods often require extensive validation data, increasing complexity and cost.

Purpose of the Study:

  • To introduce a novel, robust nonlinear identification algorithm.
  • To enhance model generalization properties.
  • To develop a fully automated model evaluation procedure.

Main Methods:

  • Utilizes the predicted residual sums of squares (PRESS) statistic.
  • Integrates PRESS computation within a forward orthogonalization process.
  • Employs forward regression for model construction.

Main Results:

  • The algorithm computes the PRESS statistic efficiently.

Related Experiment Videos

  • Achieves a model with superior generalization capabilities.
  • Eliminates the need for external validation datasets in iterative evaluation.

    • Conclusions:

    • The proposed algorithm offers a robust and automated approach to nonlinear system identification.
    • Forward orthogonalization combined with PRESS enhances model reliability.
    • This method simplifies the model selection process in nonlinear systems.