Name
Title | ||
|---|---|---|
Graduate Enrollment Prediction By An Error Back Propagation Algorithm Based On The Multi-Experiential Particle Swarm Optimization |
Abstract | ||
|---|---|---|
The graduate enrollment is influenced by the current national policy, the social needs, and the social economic status and so on. The change of the enrollment number shows the non-linearity and the complexity. In order to have better grasp of the enrollment scale and to realize the rational allocation of educational resources, we propose a Multi-Experiential Particle Swarm Optimization (MEPSO) algorithm. The algorithm is combined with the Error Back Propagation (BP) algorithm to establish a new neural network that is called the MEPSO-BP neural network. Then we present the simulation numerical studies based on several typical algorithms. The results show the MEPSO-BP algorithm improves the convergence speed and the predictive accuracy, and it can be regarded as a new method for the graduate enrollment prediction. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 11TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) | MEPSO, BP, neural network, MEPSO-BP |
Field | DocType | Citations |
Convergence (routing),Experiential learning,Educational resources,Nonlinear system,Computer science,Artificial intelligence,Artificial neural network,Particle swarm optimization,Mathematical optimization,GRASP,Algorithm,Backpropagation,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.36 | 3 | 3 |