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Deterministic learning-based neural identification and knowledge fusion.

Weiming Wu1, Jingtao Hu1, Zejian Zhu2

  • 1School of Control Science and Engineering, Shandong University, JiNan 250061, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces knowledge fusion for deterministic learning to overcome limitations in identifying system dynamics. New online and offline schemes integrate knowledge from multiple trajectories, significantly expanding learned understanding.

Keywords:
Adaptive learningDeterministic learningKnowledge fusionPersistently excitingSampled-data system

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Area of Science:

  • Control Systems Engineering
  • Machine Learning
  • Artificial Intelligence

Background:

  • Deterministic learning methods achieve local accuracy in system dynamics identification.
  • Current neural networks capture limited knowledge along individual system trajectories.

Purpose of the Study:

  • To investigate knowledge fusion for deterministic learning to integrate diverse trajectory data.
  • To develop methods for capturing broader system dynamics knowledge.

Main Methods:

  • Introduced online and offline knowledge fusion schemes for deterministic learning.
  • Developed an auxiliary information transmission strategy for online cooperative neural identification.
  • Proposed a low-complexity weight fusion algorithm for offline knowledge distillation.

Main Results:

  • Online scheme ensures exponential convergence of localized RBF network weights to true values.
  • Offline scheme successfully fuses knowledge from individual trajectories using a novel algorithm.
  • Both methods expand the learned knowledge region without compromising performance.

Conclusions:

  • Knowledge fusion effectively integrates information from multiple trajectories in deterministic learning.
  • The proposed online and offline schemes enhance the scope of system dynamics identification.
  • This approach significantly broadens the applicability and understanding derived from deterministic learning.