人工神经网络在环棱螺体质量缺失值预测中的应用
Journal of Huazhong Agricultural University（2021）
Abstract
环棱螺育种时,往往会出现部分个体体质量数据缺失的情况.为尽可能利用育种性能优异的所有个体的信息,采用人工神经网络对来自5个地理群体(阳澄湖、江阴、官莲湖、洪湖和仙桃)的784个环棱螺的4个形态学指标(包括壳高、壳宽、壳口高和壳口宽)和体质量数据进行训练,再使用太湖群体的261个环棱螺的相应数据进行人工神经网络模型测试,建立了用于环棱螺体质量缺失值预测的人工神经网络模型,利用该人工神经网络模型对微山湖群体的201个环棱螺缺失的体质量进行预测,并比较该方法与另外2种缺失值预测方法(即预测均数匹配法和随机森林预测法)的决定系数.结果显示,研究构建的人工神经网络模型对环棱螺体质量缺失值预测的决定系数为0.96,明显高于预测均数匹配法(0.87)和随机森林预测法(0.85)的决定系数.以上结果表明,本研究建立的人工神经网络模型可以用于环棱螺体质量缺失值的预测.
More求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined