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Related Experiment Videos

Dual-orthogonal radial basis function networks for nonlinear time series prediction.

X Hong1, Steve A. Billings

  • 1Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2003
PubMed
Summary

A novel Dual-orthogonal Radial Basis Function (RBF) Network (DRBF) improves nonlinear time series prediction by modifying distance metrics. This new approach enhances accuracy for complex, correlated data, outperforming conventional methods.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Conventional Radial Basis Function (RBF) networks use Euclidean distance, which is suboptimal for highly correlated time series data.
  • Lagged system outputs in time series prediction often exhibit strong correlations, challenging standard distance-based RBF network approaches.

Purpose of the Study:

  • Introduce a new Dual-orthogonal RBF Network (DRBF) structure for improved nonlinear time series prediction.
  • Address the limitations of Euclidean distance metrics in RBF networks when dealing with correlated time series inputs.

Main Methods:

  • The DRBF network modifies the distance metric using a classification function based on estimation data.
  • Training involves a two-stage process: learning classification basis functions and input nodes, followed by regressor selection and weight learning.

Related Experiment Videos

  • A forward Orthogonal Least Squares (OLS) selection procedure is employed for selecting important input nodes and centers.

    • Main Results:

    • Simulation results demonstrate the effectiveness of the DRBF network for both single-step and multi-step ahead predictions.
    • The DRBF approach shows improved performance on a test dataset compared to conventional methods.

    Conclusions:

    • The DRBF network offers a more appropriate and effective approach for nonlinear time series prediction, especially with correlated data.
    • The modified distance metric and two-stage training process contribute to enhanced predictive accuracy.