Improved broad learning system: partial weights modification based on BP algorithm

Abstract

Although breakthrough achievements of deep learning have been made in different areas, there is no good idea to prevent the time-consuming training process. Single-layer feedforward neural networks (e.g. BLS) are used to reduce the training time. However, with the decrease of training time, the accuracy degradation has emerged. In view of the limitation of random generation of connection parameters between feature nodes and enhancement nodes, this paper presents an algorithm (IBLS) based on BLS and backpropagation algorithm to learn the weights between feature nodes and enhancement nodes. Experiments over NORB and MNIST data sets show that the improved broad learning system achieves acceptable results.