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Deep Neural Networks for Image-Based Dietary Assessment
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Fractional-Order Deep Backpropagation Neural Network.

Chunhui Bao1, Yifei Pu1, Yi Zhang1

  • 1College of Computer Science, Sichuan University, Chengdu 610065, China.

Computational Intelligence and Neuroscience
|August 2, 2018
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Summary
This summary is machine-generated.

This study introduces a novel fractional-order deep backpropagation neural network with L2 regularization, optimized using fractional gradient descent. The model demonstrates deterministic convergence and effectively prevents overfitting on the MNIST dataset.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Fractional Calculus

Background:

  • Fractional calculus applications in artificial neural networks are gaining traction.
  • Deep backpropagation (BP) neural networks are widely used but can overfit.
  • L2 regularization is a common technique to mitigate overfitting.

Purpose of the Study:

  • To propose a novel fractional-order deep backpropagation neural network model.
  • To incorporate L2 regularization for enhanced model performance.
  • To analyze the convergence properties and overfitting avoidance capabilities of the proposed network.

Main Methods:

  • Development of a fractional-order deep BP neural network model.
  • Optimization using the fractional gradient descent method with Caputo derivative.
  • Analysis of convergence conditions and the influence of L2 regularization via fractional-order variational methods.
  • Experimental validation on the MNIST dataset.

Main Results:

  • The proposed fractional-order deep BP network with L2 regularization was successfully implemented.
  • Necessary conditions for the convergence of the network were established.
  • The impact of L2 regularization on convergence was analyzed.
  • Experimental results on the MNIST dataset confirmed deterministic convergence and effective overfitting prevention.

Conclusions:

  • The proposed fractional-order deep BP neural network with L2 regularization offers a robust approach to machine learning tasks.
  • The model exhibits deterministic convergence and superior generalization capabilities.
  • This research contributes to the advancement of fractional calculus applications in deep learning.