基于预测频率的自适应神经网络集成

IF 4.8 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Design and Engineering Pub Date : 2023-07-11 DOI:10.1093/jcde/qwad071
Ungki Lee, Namwoo Kang
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引用次数: 0

摘要

神经网络集成可以减少神经网络的预测方差,提高预测精度。对于数据集不足的高度非线性问题,神经网络模型的预测精度变得不稳定,导致集成精度下降。因此,本研究提出了一种基于预测频率的集合,该集合识别岩心预测值,这些预测值是集合中使用的岩心预测成员,并且期望集中在真实响应附近。基于预测频率的集成通过对频率分布进行统计分析,对多个神经网络模型支持的核心预测值进行分类,频率分布是对给定预测点的多个神经网络模型得到的预测值的集合。基于预测频率的集合搜索包含某一频率以上的预测值的预测值范围,通过排除低准确率的预测值并处理最频繁值的不确定性来提高预测性能。为了有效提高基于预测频率的集成的预测性能,提出了一种基于核心预测值方差计算的核心预测方差顺序添加样本的自适应采样策略。各种实例研究结果表明,基于预测频率的集成预测精度高于Kriging和其他现有集成方法。此外,与先前开发的基于空间填充和基于方差的预测策略相比,所提出的自适应采样策略有效地提高了基于频率的预测集成的预测性能。
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Adaptive neural network ensemble using prediction frequency
Neural network (NN) ensembles can reduce large prediction variance of NN and improve prediction accuracy. For highly nonlinear problems with insufficient data set, the prediction accuracy of NN models becomes unstable, resulting in a decrease in the accuracy of ensembles. Therefore, this study proposes a prediction frequency-based ensemble that identifies core prediction values, which are core prediction members to be used in the ensemble and are expected to be concentrated near the true response. The prediction frequency-based ensemble classifies core prediction values ​​supported by multiple NN models ​​by conducting statistical analysis with a frequency distribution, which is a collection of prediction values ​​obtained from various NN models for a given prediction point. The prediction frequency-based ensemble searches for a range of prediction values that contains prediction values above a certain frequency, and thus the predictive performance can be improved by excluding prediction values with low accuracy ​​and coping with the uncertainty of the most frequent value. An adaptive sampling strategy that sequentially adds samples based on the core prediction variance calculated as the variance of the core prediction values is proposed to improve the predictive performance of the prediction frequency-based ensemble efficiently. Results of various case studies show that the prediction accuracy of the prediction frequency-based ensemble is higher than that of Kriging and other existing ensemble methods. In addition, the proposed adaptive sampling strategy effectively improves the predictive performance of the prediction frequency-based ensemble compared with the previously developed space-filling and prediction variance-based strategies.
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来源期刊
Journal of Computational Design and Engineering
Journal of Computational Design and Engineering Computer Science-Human-Computer Interaction
CiteScore
7.70
自引率
20.40%
发文量
125
期刊介绍: Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering: • Theory and its progress in computational advancement for design and engineering • Development of computational framework to support large scale design and engineering • Interaction issues among human, designed artifacts, and systems • Knowledge-intensive technologies for intelligent and sustainable systems • Emerging technology and convergence of technology fields presented with convincing design examples • Educational issues for academia, practitioners, and future generation • Proposal on new research directions as well as survey and retrospectives on mature field.
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