Incremental Multilayer Broad Learning System With Stochastic Configuration Algorithm for Regression

Abstract

Broad learning system (BLS) is a novel randomized learning framework which has a faster modeling efficiency. Although BLS with incremental learning has a better extendibility for updating model rapidly, the incremental mode of BLS lacks self-supervision mechanism which cannot adjust the structure adaptively. Learning from the idea of stochastic configuration network (SCN), a novel incremental multi-layer broad learning system based on stochastic configuration (SC) algorithm is proposed for regression, termed as IMLBLS-SC. Firstly, to improve the feature learning ability, SC algorithm is adopted to configure the parameters of enhancement nodes instead of random weights. Secondly, the multi-layer model with enhancement nodes can be added gradually according to supervision mechanism without human intervention. Thirdly, all the enhancement nodes and feature nodes are fully connected with output nodes. Finally, 2 function approximation problems and 8 classical datasets are selected to verify the regression performance of IMLBLS-SC, experimental results demonstrate that IMLBLS-SC outperforms the RVFLN, SCN, BLS and BSCN.