Incremental Bayesian broad learning system and its industrial application

Abstract

Broad learning system (BLS) is viewed as a class of neural networks with a broad structure, which exhibits an efficient training process through incremental learning. An incremental Bayesian framework broad learning system is proposed in this study, where the posterior mean and covariance over the output weights are both derived and updated in an incremental manner for the increment of feature nodes, enhancement nodes, and input data, respectively, and the hyper-parameters are simultaneously updated by maximizing the evidence function. In such a way, the scale of matrix operations is capable of being effectively reduced. To verify the performance of this proposed approach, a number of experiments by using four benchmark datasets and an industrial case are carried out. The experimental results demonstrate that the proposed method can not only achieve a better outcome compared to the classical BLS and other comparative algorithms but also incrementally remodel the system.