太阳射电频谱爆发识别的元学习方法

doi:10.12305/j.issn.1001-506X.2022.08.04

Abstract

Abstract:

Solar radio spectrum images play an important role in the observation, research and prediction of solar activity and space weather. The solar radio broadband dynamic spectrometer is the main equipment for observing solar radio signals in China. However, it is affected by the window time, observation equipment and the law of solar activities, and the collected spectrum data has the problem of few effective samples. In view of this situation, a few-shot learning method based on meta-learning and transfer-learning is proposed to improve the classification performance of solar radio spectrum images. First, the model learns meta-knowledge on the meta-learning benchmark dataset, then defines the model for a few sample recognition of radio spectrum images, and finally transfers the meta-knowledge to the classification task of the spectrum image dataset. Through experimental analysis and performance comparison of multiple meta-learning methods, it proves that the method in this paper is advanced and effective.

Key words: solar radio spectrum, few-shot learning, meta-learning, knowledge transfer

CLC Number:

TP391.1

Juncheng GUO, Gang WAN, Xinjie HU, Fabao YAN, Shuai WANG. Meta-learning method for solar radio spectrum burst recognition[J]. Systems Engineering and Electronics, 2022, 44(8): 2410-2418.

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References 33

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参数	类型
参数	Non-burst	Burst	Calibration	总计
数量	3 335	579	494	4 408
尺寸	240×2 520	240×2 520	240×2 520	240×2 520

参数	类型
参数	MiniImageNet	Omniglot	射电频谱数据
原始尺寸	84×84×3	105×105×1	240×2 520×1
实验尺寸	60×60×3	60×60×1	60×60×3(1)
类别数量	64	964	3
每类数量	600	20	600

GPU	CPU	主板	内存
NVIDIA TITAN RTX 24G	Intel i9-9900k	Intel Z390 UD	64G

模型	base class	K	dist	inner_train/ val_steps	layerr_size	batch_size (episodes)	inner_lr/ meta_lr
Reptile^[31]	MiniImageNet	1、5、20	N/A	1/3	576	32	0.4/0.001
Prototypical Nets^[22]	MiniImageNet	1、5、20	L2	N/A	N/A	100	N/A
Matching Nets^[23]	MiniImageNet	1、5、20	Cosine	N/A	1 600	100	N/A
Meta-learner LSTM^[28]	MiniImageNet	1、5、20	N/A	N/A	512	64	0.5/0.001
MAML^[27]	MiniImageNet	1、5	N/A	1/3	576	32	0.4/0.001
MAML	MiniImageNet	20	N/A	1/3	576	16	0.4/0.005
Prototypical Nets	Omniglot	1、5	L2	N/A	N/A	100	N/A
Matching Nets	Omniglot	1、5	Cosine	N/A	64	100	N/A

模型	3-way accracy/%
模型	1-shot	5-sot	20-shot
Reptile	60.28±0.66	69.59±1.53	70.52±1.64
Prototypical Nets	54.68±0.94	73.20±0.01	80.56±0.33
Matching Nets	53.39±0.58	63.80±0.46	68.69±0.45
MAML	75.19±0.55	88.57±0.55	85.72±0.89
Meta-learner LSTM	72.95±0.69	85.46±0.84	91.60±0.73

Meta-learning method for solar radio spectrum burst recognition

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