A Dynamic Network Resource Demand Predicting Algorithm Based on Incremental Design of RBF

Nowadays, the research on network mapping is mostly limited to static resource allocation. In fact, the user’s demand of network resources changes dynamically over time. Therefore, how to predict the time-varying demand of users and allocate appropriate resources becomes an important way to improve resource utilization. As a fully connected artificial neural network (ANN), the RBFN (Radial Basis Function Network) has diagnostic, predictive and classification functions. However, due to the excessive use of hidden RBF units during training process, it suffers from expensive core inner product calculations and long training time. This paper proposes a dynamic network resource demand predicting algorithm based on RBF incremental design (GSO-INC-RBFDM). In the network mapping, the group search optimizer (GSO) is used to optimize the mapping scheme, and then the radial basis function (RBF) of the incremental construction is used to predict the time-varying demand of the user. GSO-INC-RBFDM based on incremental design of RBF can construct a compact neural network structure, which not only accelerates the training speed, but also improves the predictive accuracy. Simulation experiments show, compared with traditional algorithms and the original RBF, GSO-INC-RBFDM have lower cost, higher acceptance rate and network revenue.