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Particle Swarm Optimization for Deep Convolutional Neural Networks

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thesis
posted on 2024-11-26, 04:45 authored by Feihu Han
This thesis proposes a hybrid adaptive learning algorithm called Particle Optimized Gradient Descent (POGD) to improve the convergence of convolutional neural networks (CNNs) in image classification. The proposed algorithm has a better protective effect from the local minimum and achieves a higher accuracy with fewer epochs. This research also emphasizes the importance of adjusting the parameters.

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Pagination

p.

Open access

  • Yes

Language

eng

Degree type

Masters

Degree name

MEng

Copyright notice

All rights reserved

Editor/Contributor(s)

Sui Yang Khoo, Michael Norton

Faculty

Faculty of Science, Engineering and Built Environment

School

School of Engineering

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