Optimize CNN parameters based on grey wolf algorithm
Author: 
Xianbin OuAuthors Info & Claims
Pages 36 - 40
Abstract
Recent studies have found that CNN can show quite good performance in various tasks such as recognition and classification, However CNN calculations require too many parameters, and the process of training is difficult. A large number of parameters may lead to over-fitting. In order to reduce the number of parameters and improve the accuracy of prediction, this Article proposed a model, on the basis of the traditional CNN algorithm, the gray wolf algorithm is used to automatically find optimal parameters, and the minimum error rate is used as the judgment criterion to find the optimal solution of the number of Convolutional layers and convolution kernels.
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Published In
November 2023
902 pages
ISBN:9798400716485
DOI:10.1145/3653081
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 May 2024
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IoTAAI 2023
IoTAAI 2023: 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence
November 24 - 26, 2023
Nanchang, China
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