Abstract: For the online learning problem of dynamic data flow, the traditional online BLS ( Broad Learning System) algorithm can not accurately capture the latest change trend of the data. Therefore, two online BLS algorithms with forgetting mechanism, one is based on forgetting factor ( FF-OBLS: Online Broad Learning System based on Forgetting Factor) and other is based on sliding window ( SW-OBLS: Online Broad Learning System based on Sliding Window), are proposed. FF-OBLS reflects the different contributions of old and new samples to the learning model by adding forgetting factors to old samples in the online learning process, SW-OBLS eliminates the impact of old samples on the learning model by deleting old samples in the online learning process, so as to enable the learning model to accurately analyze and predict the subsequent trend of dynamic data flow. In order to verify the effectiveness of the proposed two algorithms, dynamic regression data sets are used in the experiment. The experimental results show that the online BLS models with forgetting mechanism are better than the traditional online BLS model in the perspective of prediction accuracy and time cost, therefore they are more suitable to deal with dynamic data flow problems.

Key words: broad learning system,  , dynamic data flow,  , forgetting mechanism,  , forgetting factor,  , sliding window