Adaptive Variable Weight Accumulation AVWA-DGM(1,1) Model Based on Particle Swarm Optimization

The development of higher education is an extremely important issue. It is the source of the country's technological innovation and the realization of innovation and development, especially in China, where higher education is still at an exploratory stage. Aiming at the shortcoming that the classical DGM (1,1) model accumulates the raw data series with the weight of constant "1", this paper proposes an adaptive variable weight accumulation optimization DGM (1,1) model, abbreviated as AVWA-DGM (1,1) model. Taking the enrollment numbers of postgraduate, master degree, undergraduate and junior college student and undergraduates students in China as numerical examples, the DGM (1,1) model and AVWA-DGM (1,1) model are established to simulate and predict respectively, and the weighted coefficients of AVWA-DGM (1,1) model are optimized and solved by particle swarm algorithm. The results show that the AVWA-DGM(1,1) model has higher simulation and prediction accuracy than the classical DGM(1,1) model in the four numerical examples provided in this paper. It can be seen that the adaptive accumulation of the raw data sequence by the particle swarm optimization algorithm can make the first order accumulation sequence more in line with the requirements of the DGM (1,1) model on the data features, thereby improving the simulation and prediction accuracy.