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A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

Xia Hong

    IEEE Transactions on Neural Networks
    |July 22, 2006
    PubMed
    Summary
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    This study introduces a novel Box-Cox transformation-based radial basis function (RBF) neural network for efficient system identification. The method enhances accuracy and computational speed compared to support vector machine regression.

    Area of Science:

    • Computational intelligence
    • Machine learning
    • Nonlinear system modeling

    Background:

    • Radial basis function (RBF) neural networks are effective for system identification.
    • Box-Cox transformation can improve model linearity and data distribution.
    • Efficient model identification is crucial for complex systems.

    Discussion:

    • A novel Box-Cox transformation-based RBF neural network is proposed.
    • The network utilizes QR decomposition for initial model base derivation.
    • A fast identification algorithm employing the Gauss-Newton method is developed for Box-Cox transformation estimation.
    • Computational efficiency is achieved by exploiting matrix block decomposition properties.

    Key Insights:

    • The proposed RBF neural network demonstrates good generalization and sparsity.

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  • The integration of Box-Cox transformation enhances model performance.
  • The developed identification algorithm is computationally efficient.

    • Outlook:

    • Further applications in complex nonlinear system modeling.
    • Potential for integration with other machine learning techniques.
    • Exploration of advanced optimization strategies for parameter tuning.