Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6): 134-141.doi: 10.11896/jsjkx.210500119

• Database & Big Data & Data Science • Previous Articles     Next Articles

Adaptive Weight Based Broad Learning Algorithm for Cascaded Enhanced Nodes

CAI Xin-yu, FENG Xiang, YU Hui-qun   

  1. Department of Computer Science and Engineering,East China University of Science and Technology,Shanghai 200237,China
    Shanghai Smart Energy Engineering Technology Research Center,Shanghai 200237,China
  • Received:2021-05-17 Revised:2021-10-18 Online:2022-06-15 Published:2022-06-08
  • About author:CAI Xin-yu,born in 1998,postgra-duate,is a member of China Computer Federation.His main research interests include swarm intelligence and broad learning.
    FENG Xiang,born in 1977,Ph.D,professor,is a member of China Computer Federation,Her main research interests include artificial intelligence,swarm intelligence and evolutionary computing,and big data intelligence.
  • Supported by:
    National Natural Science Foundation of China(61772200,61772201,61602175),Shanghai Pujiang Talent Program(17PJ1401900) and Shanghai Economic and Information Commission “Special Fund for Information Development”(201602008).

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Abstract: 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.

Key words: Broad learning, Data enhancement, Time series prediction, Weight factor

CLC Number: 

  • TP183
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