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Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with

Xia Hong1, Chris J Harris, Sheng Chen

  • 1Cybernetic Intelligence Research Group, Department of Cybernetics, University of Reading, Reading, RG6 6AY, UK. x.hong@reading.ac.uk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 17, 2004
PubMed
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A novel algorithm constructs robust neurofuzzy models for unknown dynamical systems using fuzzy rules. It enhances transparency and model interpretability through orthogonal subspace decomposition and advanced G-S methods.

Area of Science:

  • Engineering
  • Computer Science
  • Control Theory

Background:

  • Dynamical systems modeling often requires interpretable and robust approaches.
  • Existing methods may struggle with a priori unknown systems and extracting transparent knowledge from fuzzy rules.

Purpose of the Study:

  • Introduce a new robust neurofuzzy model construction algorithm for unknown dynamical systems.
  • Enhance model transparency and interpretability by establishing a link between fuzzy rules and matrix feature subspaces.
  • Achieve maximized model robustness and sparsity.

Main Methods:

  • Utilized a Takagi-Sugeno (T-S) inference mechanism for rule base construction.
  • Developed a robust extended Gram-Schmidt (G-S) method incorporating regularization and D-optimality experimental design.

Related Experiment Videos

  • Employed locally regularized orthogonal least squares and D-optimality for subspace-based rule selection and information extraction.

    • Main Results:

    • Established a one-to-one mapping between fuzzy rule bases and model matrix feature subspaces, enabling knowledge extraction.
    • Decomposed model rule bases into orthogonal subspaces for enhanced transparency and interpretation of rule base energy.
    • Demonstrated the effectiveness of the automatic, weighted D-optimality cost function-driven model construction procedure through numerical examples.

    Conclusions:

    • The proposed algorithm effectively models unknown dynamical systems with enhanced robustness and transparency.
    • Orthogonal subspace decomposition provides a clear interpretation of fuzzy rule bases.
    • The integration of regularization and D-optimality ensures efficient and automatic model construction.