Broad Learning System with Proportional-Integral-Differential Gradient Descent

Weidong Zou, Yuanqing Xia, Weipeng Cao*, Zhong Ming

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Broad learning system (BLS) has attracted much attention in recent years due to its fast training speed and good generalization ability. Most of the existing BLS-based algorithms use the least square method to calculate its output weights. As the size of the training data set increases, this approach will cause the training efficiency of the model to be seriously reduced, and the solution of the model will also be unstable. To solve this problem, we have designed a new gradient descent method (GD) based on the proportional-integral-differential technique (PID) to replace the least square operation in the existing BLS algorithms, which is called PID-GD-BLS. Extensive experimental results on four benchmark data sets show that PID-GD can achieve faster convergence rate than traditional optimization algorithms such as Adam and AdaMod, and the generalization performance and stability of the PID-GD-BLS are much better than that of BLS and its variants. This study provides a new direction for BLS optimization and a better solution for BLS-based data mining.

Original languageEnglish
Title of host publicationAlgorithms and Architectures for Parallel Processing - 20th International Conference, ICA3PP 2020, Proceedings
EditorsMeikang Qiu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages219-231
Number of pages13
ISBN (Print)9783030602444
DOIs
Publication statusPublished - 2020
Event20th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2020 - New York, United States
Duration: 2 Oct 20204 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12452 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2020
Country/TerritoryUnited States
CityNew York
Period2/10/204/10/20

Keywords

  • Broad learning system
  • Neural networks with random weights
  • Optimization algorithms
  • Proportional-integral-differential
  • Randomized algorithms

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