Load Forecasting for City Cloud NSSA System Structure
Jing Chang, Dong Liu · 2019
Substantial progress has been achieved in improving the accuracy and speed of load forecasting for city cloud network security situation awareness (NSSA) system structure. In this paper, the radial basis function (RBF) neural network for awareness was optimized by using the alternating gradient algorithm, so as to forecast the load for city cloud NSSA. The modified simulation algorithm based on experimental data was powerful in load forecasting for city cloud NSSA. Compared with the conventional gradient algorithm for network awareness, the modified algorithm featured faster convergence speed and higher load forecasting accuracy.