Network Safety Evaluation based on Pso-Rbf Neural Network

被引：0

作者：

Song Hai-Sheng ^{[1
]}

机构：

[1] Northwest Normal Univ, Phys & Elect Engn Coll, Lanzhou 730070, Peoples R China

来源：

FIFTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2012): ALGORITHMS, PATTERN RECOGNITION AND BASIC TECHNOLOGIES | 2013年 / 8784卷

关键词：

network safety; neural network; RBF; evaluation technology; PARTICLE SWARM OPTIMIZATION; ALGORITHM;

D O I：

10.1117/12.2014149

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：4

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