文献类型：期刊文献

英文题名：Broad learning algorithm of cascaded enhancement nodes based on phase space reconstruction

作者：Cai, Xinyu[1,2];Feng, Xiang[1,2];Yu, Huiqun[1,2]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Engn Res Ctr Smart Energy, Shanghai 200237, Peoples R China

年份：2023

卷号：53

期号：2

起止页码：2321

外文期刊名：APPLIED INTELLIGENCE

收录：;EI(收录号：20221912080816);WOS:【SCI-EXPANDED(收录号:WOS:000791661100004)】；

基金：This work is supported by the Key Program of National Natural Science Foundation of China (62136003), the National Natural Science Foundation of China (61772200 and 61772201), Shanghai Pujiang Talent Program (17PJ1401900), Shanghai Economic and Information Commission "Special Fund for Information Development" (XX-XXFZ-02-20-2463).

语种：英文

外文关键词：Broad learning system; Time series prediction; Weight factor; Cascade of enhancement nodes

摘要：In the era of intelligence, we need to carry out continuous autonomous learning and optimization on the 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 based on 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 retain part of the historical information. In the part of 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 the modeling, 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.