An improved BP algorithm over out-of-order streams for big data

Due to the difficulty of getting the association rules over out-of-order streams for big data, a new improved BP algorithm based on dynamic adjustment is proposed. We firstly use a dynamic adaptive structural adjustment mechanism to change the network training structure according to the environmental requirements, which can automatically remove invalid training node, and optimize the iterative training process. Secondly, we adjust three factors (i.e. learning index, momentum factor and scaling factor) during the learning process to speed up the learning response, and to enhance the stability of the network. Simulation results show that compared with traditional BP algorithm, this algorithm can get more convergence times,the convergence rate can be improved effectively, and finally obtain the association rules over out-of-order data streams.