We propose an adaptive Kohonen Self-Organizing Maps and Radial Basis Function Network-based method (KR) for network blocking forecasting in the paper.It shows that there are some problems in the network blocking forecasting now

especially when the data set is just small.Therefore

for achieving high accuracy in the network blocking forecasting

it is necessary to consider the relationships between each data within the original data set in the forecasting process.Now to get more valuable position information

a series of processes including Kohonen neural network and RBF network is proposed to meet the types of different data.The process makes the network can meet the different kinds of data.In this application to a citys network blocking forecasting

we investigate KRs and two other algorithms performance on a original data set.The comparison of experimental results shows that KR is better location performance than others.