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Chen, C.L.P., Liu, Z. and Feng, S. (2019) Universal Approximation Capability of Broad Learning System and Its Structural Variations. IEEE Transactions on Neural Networks and Learning Systems, 30, 1191-1204.
https://doi.org/10.1109/tnnls.2018.2866622

has been cited by the following article:

• TITLE: Decentralized Elastic-Net Broad Learning System over Directed Graphs

  AUTHORS: Jinyan Liang, Manrui Zhou

  KEYWORDS: Broad Learning System (BLS), Directed Graphs, AB/Push-Pull, Distributed Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.14 No.7, July 27, 2026

  ABSTRACT: In this paper, we propose a decentralized version of elastic-net broad learning systems, focusing on the case when directed graphs characterize the communication between agents. This novel proposed algorithm is referred to as Directed-DENBLS, which involves decentralized learning for feature fine-tuning and decentralized learning for output weights. However, considering that in a directed graph, it may not be possible to construct a doubly stochastic matrice in a distributed manner. So the proposed algorithm is built on the AB/Push-Pull algorithm, which creates a row stochastic matrix and a column stochastic matrix to address the resulting problem in a decentralized manner. Simulation results on the datasets demonstrate the effectiveness of the proposed Directed-DENBLS algorithm.