基于新的三重激活函数的目标检测

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS Systems Science & Control Engineering Pub Date : 2022-06-23 DOI:10.1080/21642583.2022.2091060
Guanyu Chen, Q. Wang, Xiang Li, Yanyun Zhang
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引用次数: 1

摘要

激活函数作为神经网络的重要组成部分,在神经网络的模型训练中起着非常重要的作用。本文分析了现有常用激活函数的现状、优缺点,提出了一种新的激活函数,并将其应用于目标检测。为了测试新激活函数的性能,本文将其与ReLU激活函数在各种神经网络和数据集上进行了比较,不仅分析了激活函数本身的性能,还验证了激活函数在目标检测中的有效性。
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Target detection based on a new triple activation function
As one of the important parts of Neural Network, activation function plays a very important role in model training in Neural Network. In this paper, the status quo, advantages and disadvantages of the existing common activation functions are analysed, and a new activation function is proposed and applied to target detection. To test the performance of the new activation function, this paper compares it with the ReLU activation functions on a variety of Neural Networks and data sets, and not only analyses the performance of the activation function itself but also verifies the effectiveness of the activation function in target detection.
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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