Considering to establish a national ammunition warehouse data processing of a large number of real-time monitoring information and real-time transmission to the requirement of server hardware resources, it was proposed to build a national ammunition warehouse distributed test system based on grid technology. Experiments show that the system has a scalable computing capacity, high computational efficiency, low cost, and good application prospect.

Improvement on defining hidden layer’s clustering centers of RBF network is greatly important for the network modeling and prediction, especially for multivariate time series. This paper firstly uses a linear function and a nonlinear function respectively to detect the linear correlations and the nonlinear correlations of the time series. And a small data set which includes effective information of the system is defined. Then, a local search procedure is introduced to optimize K-means clustering algorithm, which is aimed at quickly and effectively adjusting hidden layer’s clustering centers. Finally, input other training samples to determine network weights, LS or its improved method can be selected. Simulation results show that compared with that of conventional RBF network, with the same number of hidden layer’s nodes, the RBF network model in this paper gets better prediction accuracy.