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High-Order Neural Network structure selection for function approximation applications using Genetic Algorithms.

G A Rovithakis1, I Chalkiadakis, M E Zervakis

  • 1Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54006 Thessaloniki, Greece.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
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This study introduces a novel Genetic Algorithm (GA) approach for determining the structure of High Order Neural Networks (HONNs). The method effectively addresses function approximation challenges by integrating structural learning with stable parametric learning.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Function approximation is a core problem in machine learning.
  • Existing neural network research often assumes a known network structure.
  • Parametric uncertainty (weight determination) is well-studied, but structural learning is less explored.

Purpose of the Study:

  • To present an algorithmic approach for determining the structure of High Order Neural Networks (HONNs).
  • To address function approximation problems using a method that combines structural and parametric learning.
  • To provide a robust method for neural network design.

Main Methods:

  • Utilizing a Genetic Algorithm (GA) for structural learning of HONNs.
  • Developing a stable update law to ensure reliable parametric learning.
  • Integrating GA-based structure selection with a guaranteed learning process.

Main Results:

  • Demonstrated the performance of the proposed algorithmic approach through simulation results.
  • Provided insights into the effectiveness of the GA-based method for HONN structure determination.
  • Successfully applied the method to a function approximation problem.

Conclusions:

  • The proposed GA-based approach offers an effective solution for the structural learning of HONNs.
  • The method successfully integrates structural optimization with stable parametric learning.
  • This work contributes a valuable tool for tackling complex function approximation tasks with neural networks.