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March 29, 2026The European Physical Journal Special Topics0 citationsOpen Access

Improving Multi-Layer Perceptron Learning with Recurrence Microstates

Using recurrence microstates to improve learning of multi-layer perceptrons

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Authors

TPThiago Lima PradoRSRobison J. Santos-SilvaGSG. S. Spezzatto

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Overview

Demonstrates enhanced neural network performance in dynamical systems classification, indicating valuable interpretative tools.

Key Points

  • The aim is to improve understanding and performance of multi-layer perceptrons using recurrence analysis during learning processes.
  • Applied recurrence analysis to interpret MLP dynamics during supervised classification.
  • Examined various dynamical systems: generalized Bernoulli shift, logistic map, Lorenz attractor, and colored noise.
  • Analyzed changes in connection weight matrices due to microstates quantification.
  • Microstates quantification altered network data interpretation by changing connection weights.
  • Identified overfitting more effectively with recurrence microstates analysis.
  • Improved interpretability of MLP internal dynamics was observed for complex systems.
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Cite This Study

Prado et al. (2026) studied this question.

synapsesocial.com/papers/69c8c2a4de0f0f753b39d0a6https://doi.org/10.1140/epjs/s11734-026-02256-4
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Recurrence microstates for machine learning classification2024 · 2 citations
  2. 2Machine learning approach to detect dynamical states from recurrence measures2024 · 4 citations
  3. 3Enhancing a multilayer perceptron model for multi-state network reliability evaluation via an arc-wise architecture2026
  4. 4Recurrence Resonance -- Noise-Enhanced Dynamics in Recurrent Neural Networks2024
  5. 5Modelling Discrete States and Long-Term Dynamics in Functional Brain Networks2025