Situation assessment in complex environment is a key technology to develop advanced decision support system. To assess situation precisely in soccer match, the author analyzed the key factors affecting situation of soccer match and put forth a new situation assessment model based on experts' experience and analysis of many cases. In this model, all robots in one soccer team formed a decision group and adoption of group intelligent decision technology enhanced
performance of these robots. The group intelligent decision showed high performance, which is proved in experiments
and many international robot soccer matches.
A methodology for optimizing radial basis function (RBF) networks is proposed, which consists of the RBF network and the self-organizing map (SOM), aiming at improving the performance of the recognition and classification of novel attacks for intrusion detection. The optimal network architecture of the RBF network is determined automatically by the improved SOM algorithm, in which the centers and the number of hidden neurons are self-adjustable. The intrusion feature vectors are extracted from a benchmark dataset (the KDD-99) designed by DARPA. The experimental results demonstrate that the proposed approach to recognize network attacks performance especially in terms of both efficient and accuracy.