Incremental Multilayer Broad Learning System With Stochastic Configuration Algorithm for Regression

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 a self-supervision mechanism which cannot adjust the structure adaptively. Learning from the idea of stochastic configuration network (SCN), a novel incremental multilayer BLS based on the stochastic configuration (SC) algorithm is proposed for regression, termed as IMLBLS-SC. First, to improve the feature learning ability, the SC algorithm is adopted to configure the parameters of enhancement nodes instead of random weights. Second, the multilayer model with enhancement nodes can be added gradually according to the supervision mechanism without human intervention. Third, all the enhancement nodes and feature nodes are fully connected with output nodes. Finally, two function approximation problems and eight classical data sets are selected to verify the regression performance of IMLBLS-SC, experimental results demonstrate that IMLBLS-SC outperforms the random vector functional-link neural network, SCN, BLS, and broad SCN.

Automated summary

Broad learning system (BLS) is a faster modeling framework. However, the incremental mode lacks self-supervision mechanism, which limits its adaptability. In this study, a novel incremental multilayer BLS based on the stochastic configuration (SC) algorithm is proposed for regression, named IMLBLS-SC. Experimental results show that IMLBLS-SC outperforms other models.