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Improving generalization capabilities of dynamic neural networks.

Miroslaw Galicki1, Lutz Leistritz, Ernst Bernhard Zwick

  • 1Institute of Medical Statistics, Computer Sciences and Documentation, Friedrich Schiller University, Jena, Germany. galicki@imsid.uni-jena.de

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This study enhances continuous recurrent neural network generalization using an optimal control approach. A novel learning algorithm improves dynamic network performance and predictive accuracy.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Control Theory

Background:

  • Continuous recurrent neural networks (CRNNs) often struggle with generalization.
  • Improving the generalization capabilities of dynamic neural networks is a significant challenge.

Purpose of the Study:

  • To develop a novel learning algorithm for enhancing CRNN generalization.
  • To frame the CRNN learning task within an optimal control framework.

Main Methods:

  • The learning task is reformulated as an optimal control problem.
  • Network weights and initial states are treated as control variables.
  • A new learning algorithm is derived from a variational formulation of Pontryagin's maximum principle.

Main Results:

  • The convergence of the proposed algorithm is theoretically discussed.
  • Numerical examples demonstrate significant improvements in generalization capabilities.
  • The dynamic network's performance is enhanced post-learning.

Conclusions:

  • The proposed optimal control-based learning algorithm effectively improves CRNN generalization.
  • This approach offers a promising direction for developing more robust dynamic neural networks.