A Research on the Improvement of Dual Optimization on BP Neural Network

At present, the most widely used BP network model in traffic, agriculture and related data mining is still an important neural network algorithm model, but its performance has not been satisfactory. The convergence and prediction accuracy of the BP neural network is general and easy to fall into the local optimal solution, and these shortcomings still need to be improved continuously. Therefore, in view of the above mentioned problems, an improved BP network algorithm is proposed by combining the dynamic self-learning effect factor and the improved network activation function. Experimental results show that the proposed improved BP network scheme can greatly improve the convergence efficiency and accuracy of BP neural networks.