Parameter training method for convolutional neural networks based on improved Hausdorff-like derivative-Reference-Cited by-同舟云学术

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Parameter training method for convolutional neural networks based on improved Hausdorff-like derivative

Published:2024-03 Issue: Volume:238 Page:121659
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Jia Kai^ORCID,Gao Zhe^ORCID,Xiao Shasha^ORCID

Funder

Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program

Natural Science Foundation of Liaoning Province

Scientific Research Fund of Liaoning Provincial Education Department

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference34 articles.

1. Fractal-fractional differentiation and integration: Connecting fractal calculus and fractional calculus to predict complex system;Atangana;Chaos, Solitons & Fractals,2017

2. Characterizing the creep of viscoelastic materials by fractal derivative models;Cai;International Journal of Non-Linear Mechanics,2016

3. Fractional order systems: modeling and control applications,Vol. 72;Caponetto,2010

4. RNN / LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting;Chandriah;Multimedia Tools and Applications,2021

5. Time-space fabric underlying anomalous diffusion;Chen;Chaos, Solitons & Fractals,2006

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