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Decoding Natural Behavior from Neuroethological Embedding
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Equivalent Neural Network Optimal Coefficients Using Forgetting Factor with Sliding Modes.

Karen Alicia Aguilar Cruz1, José de Jesús Medel Juárez1, Romeo Urbieta Parrazales1

  • 1Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Avenida Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal, Col. Nueva Industrial Vallejo, Delegación Gustavo A. Madero, 07738 Ciudad de México, Mexico.

Computational Intelligence and Neuroscience
|January 7, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel parameter estimation method for Artificial Neural Networks (ANNs) in complex stochastic systems. The proposed nonconstant exponential forgetting factor improves convergence rates for nonstationary conditions.

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Area of Science:

  • Control Systems Engineering
  • Machine Learning
  • Signal Processing

Background:

  • Artificial Neural Networks (ANNs) are used for modeling complex systems.
  • Traditional methods struggle with parameter estimation in stochastic systems with nonstationary conditions.
  • Accurate parameter estimation is crucial for achieving good convergence rates.

Purpose of the Study:

  • To present a parameter estimation method for an equivalent ANN (EANN).
  • To address the challenge of recursive identification for stochastic systems with nonstationary parameters.
  • To improve convergence rates compared to traditional approximation methods.

Main Methods:

  • Recursive identification of stochastic systems with constant and nonstationary parameters.
  • Development of a nonconstant exponential forgetting factor (NCEFF) with sliding modes.
  • Implementation and comparison of identification stages using MATLAB®.

Main Results:

  • Successful recursive identification for both constant and nonstationary parameter systems.
  • The proposed NCEFF with sliding modes achieves a decreasing exponential convergence rate.
  • Demonstrated improvement in performance for nonstationary output conditions.

Conclusions:

  • The proposed NCEFF method enhances parameter estimation for stochastic systems.
  • This approach effectively handles nonstationary output system conditions.
  • The method offers a viable solution for improving ANN convergence in complex dynamic environments.