Traditional back-propagation (BP) neural networks can implement complex
nonlinear mapping relationships, and solve internal mechanism problems.
However, as number of samples increases, training BP neural
networks may consume a lot of time. For this reason, to improve the efficiency
as well as prediction accuracy of the neural network model, in this paper,
we propose an intelligent optimization algorithm, by leveraging the beetle
antennae search (BAS) strategy to optimize the weights of neural network
model, and apply it to the population prediction. A series of experiments
demonstrate the improved accuracy of the proposed algorithm over BP neural
networks. In particular, the calculation time spent of neural network model
via the proposed algorithm is only 20% of the one of BP neural network
model. Finally, we present a reasonable trend of population growth in China,
and analyze the causes of changes in population trends, which may provide an
effective basis for the department to adjust population development
strategies
The Population Prediction based on Grey Model and BP Neural Networks
