Adaptive Weight Based Broad Learning Algorithm for Cascaded Enhanced Nodes

Abstract

In the era of intelligence,continuous autonomous learning and optimization need to be carried out on the big data platform,and the first step of continuous autonomous learning is data enhancement.This paper proposes a broad learning method based on cascaded enhancement nodes,which provides a new data enhancement method for continuous autonomous learning on big data platform,and makes it possible for subsequent evolutionary optimization on the basis of learning architecture.Classical broad learning is a typical feedforward neural network,which is not suitable for modeling dynamic time series.In this paper,the feedback structure is introduced into the traditional broad learning system,which makes the enhancement nodes have memory and retains part of the historical information.In feature extraction,phase space reconstruction is used to extract more essential features of the data.At the same time,a weight factor is introduced to assign different weights to each sample according to its contribution to model during training,so as to eliminate the interference of noise and outliers to the learning process and improve the robustness of the algorithm.Experimental results show that the proposed algorithm is effective.

Keywords

• broad learning|time series prediction|weight factor|data enhancement