In the study, RBF neural network optimized by particle swarm optimization algorithm is applied to evaluate network safety. In the RBF neural network, the choice of the three parameters including the center of RBF, the width of RBF and the weight have an important influence on the classification performance of RBF neural network. Particle swarm optimization algorithm is used to select the optimal combination of the parameters of the RBF neural network parameters. The experimental results show that the network evaluation model based on PSO-RBF neural network has better evaluation performance than RBF neural network.

Date Published: 13 March 2013
PDF: 4 pages
Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87841R (13 March 2013); doi: 10.1117/12.2014149

Published in SPIE Proceedings Vol. 8784:
Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies
Yulin Wang; Liansheng Tan; Jianhong Zhou, Editor(s)