Target detection based on a new triple activation function-Reference-Cited by-同舟云学术

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Target detection based on a new triple activation function

Published:2022-06-23 Issue:1 Volume:10 Page:629-635
ISSN:2164-2583
Container-title:Systems Science & Control Engineering
language:en
Short-container-title:Systems Science & Control Engineering

Author:

Chen Guanyu¹,Wang Quanyu¹,Li Xiang²,Zhang Yanyun³

Affiliation:

1. Informatization Office, China University of Geosciences, Wuhan, People's Republic of China

2. School of Computer Science, China University of Geosciences, Wuhan, People's Republic of China

3. School of Computer Science and Information Engineering, Hubei University, Wuhan, People's Republic of China

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Control and Optimization,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/21642583.2022.2091060

Reference33 articles.

1. A survey on modern trainable activation functions

2. Shape recognition based on neural networks trained by differential evolution algorithm

3. A novel full structure optimization algorithm for radial basis probabilistic neural networks

4. A novel type of activation function in artificial neural networks: Trained activation function

5. An improved Tiny YOLOv3 for real-time object detection

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PolyLU: A Simple and Robust Polynomial-Based Linear Unit Activation Function for Deep Learning;IEEE Access;2023

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