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Related Experiment Videos

Genetic programming of polynomial harmonic networks using the discrete Fourier transform.

Nikolay Y Nikolaev1, Hitoshi Iba

  • 1Dept. of Math. and Computing Sciences, Goldsmiths College, University of London, New Cross, London SE14 6NW United Kingdom. nikolaev@mcs.gold.ac.uk

International Journal of Neural Systems
|November 9, 2002
PubMed
Summary

This study introduces polynomial harmonic networks, a novel hybrid system for time series processing. This genetic programming approach demonstrates superior performance compared to existing methods.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Intelligence

Background:

  • Multilayer feed-forward neural networks are widely used for time series processing.
  • Traditional neural network activation functions can limit performance with complex frequency data.
  • Genetic programming offers a powerful framework for evolving network structures and functions.

Purpose of the Study:

  • To present a novel hybrid genetic programming system for evolving polynomial harmonic networks.
  • To investigate the efficacy of incorporating polynomial activation functions with harmonic inputs.
  • To evaluate the performance of the proposed system in time series processing.

Main Methods:

  • Development of a genetic programming system to evolve polynomial harmonic networks.

Related Experiment Videos

  • Utilizing discrete Fourier transform to derive harmonics with non-multiple frequencies.
  • Employing tree-structured topology for efficient evolutionary structural search.
  • Comparing the proposed system against traditional genetic programming and harmonic GMDH algorithms.

    • Main Results:

    • The evolved polynomial harmonic networks demonstrated superior performance in time series processing.
    • The hybrid system outperformed evolutionary polynomial manipulation, Koza-style genetic programming, and harmonic GMDH.
    • The novel approach effectively handles harmonics with non-multiple and irregular frequencies.

    Conclusions:

    • Polynomial harmonic networks represent a promising advancement in neural network architectures for time series analysis.
    • The proposed genetic programming system offers an effective method for evolving complex neural network structures.
    • This hybrid approach provides a robust solution for processing complex time series data with irregular frequency components.