文献类型：期刊文献

中文题名：自适应权重的级联增强节点的宽度学习算法

英文题名：Adaptive Weight Based Broad Learning Algorithm for Cascaded Enhanced Nodes

作者：蔡欣雨[1,2];冯翔[1,2];虞慧群[1,2]

机构：[1]华东理工大学计算机科学与工程系,上海200237;[2]上海智慧能源工程技术研究中心,上海200237

年份：2022

卷号：49

期号：6

起止页码：134

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：国家自然科学基金(61772200,61772201,61602175);上海市浦江人才计划(17PJ1401900);上海市经信委“信息化发展专项资金”(201602008)。

语种：中文

中文关键词：宽度学习;时序预测;权重因子;数据增强

外文关键词：Broad learning;Time series prediction;Weight factor;Data enhancement

摘要：进入智能化时代,需要在大数据平台上进行持续自主学习和优化,而持续自主学习的第一步就是进行数据增强。文中提出基于级联增强节点的宽度学习方法,为大数据平台上的持续自主学习提供了新的数据增强方法,也为后续在学习架构基础上的演化优化提供了可能。以时序预测问题为依托,但由于经典宽度学习是典型的前馈神经网络,并不适合建模动态时间序列,因此在传统的宽度学习系统中引入反馈结构,将增强节点层顺序连接,使得增强节点具有记忆性,能够保留部分历史信息。在进行特征提取时,采用了相空间重构来提取数据更本质的特征;同时,引入了权重因子,在训练时依据每个样本对模型的贡献度,为其独立分配不同的权重,从而消除噪声和离群点对学习过程的干扰,提高算法的预测准确率以及鲁棒性。实验结果表明所提算法是有效的。
In the era of intelligence,continuous autonomous learning and optimization need to be carried out on the big data platform,and the first step of continuous autonomous learning is data enhancement.This paper proposes a broad learning method based on cascaded enhancement nodes,which provides a new data enhancement method for continuous autonomous learning on big data platform,and makes it possible for subsequent evolutionary optimization on the basis of learning architecture.Classical broad learning is a typical feedforward neural network,which is not suitable for modeling dynamic time series.In this paper,the feedback structure is introduced into the traditional broad learning system,which makes the enhancement nodes have memory and retains part of the historical information.In feature extraction,phase space reconstruction is used to extract more essential features of the data.At the same time,a weight factor is introduced to assign different weights to each sample according to its contribution to model during training,so as to eliminate the interference of noise and outliers to the learning process and improve the robustness of the algorithm.Experimental results show that the proposed algorithm is effective.