Research on risk assessment model of information security based on particle swarm algorithm -RBF neural network

2010 ◽  
Author(s):  
Niu Honghui ◽  
Shang Yanling
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Information Security ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Assessment Model ◽  
Particle Swarm Algorithm ◽  
Risk Assessment Model ◽  
Swarm Algorithm

A risk assessment model for information security

2011 ◽  
Vol 5 (2) ◽  
pp. 158
Author(s):  
June Wei ◽  
Lai C. Liu ◽  
Kai S. Koong ◽  
Yi Li
Keyword(s):  
Risk Assessment ◽  
Information Security ◽  
Assessment Model ◽  
Risk Assessment Model

Risk Assessment for Remanufacturing System Based on Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 1382-1385 ◽  
Author(s):  
Qiu Lian Wang ◽  
Cong Bo Li
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Assessment Model ◽  
The Internet ◽  
Risk Assessment Model ◽  
Key Indicators ◽  
The Relationship ◽  
Evaluating Method

To provide referenced risk assessment model for implementing remanufacturing program in enterprise, a set of evaluating indicators was proposed according to the characteristics of the remanufacturing program’s life cycle, which includes acquisition, assessment, disassembly, reproducing and reprocessing phases; And Back Propagation neural network (BPNN) was applied to measure the risk of the remanufacturing system as evaluating method; In addition, the influence of the evaluating indicators on the output was calculated by the Relationship Function between the networked weights, so the key indicators can be found out. The risk assessment model is trained by five samples obtained from the Internet, and is verified by the case of one machining tools company.

Tuning the Structure and Parameters of a Neural Network by Using Cooperative Quantum Particle Swarm Algorithm

2011 ◽  
Vol 48-49 ◽  
pp. 1328-1332 ◽  
Author(s):  
Qi Feng Tang ◽  
Liang Zhao ◽  
Rong Bin Qi ◽  
Hui Cheng ◽  
Feng Qian
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Training Algorithms ◽  
Approximation Accuracy ◽  
Quantum Genetic Algorithm ◽  
Cooperation Mechanism ◽  
Swarm Algorithm ◽  
Redundant Structure

In this paper, a cooperative quantum genetic algorithm-particle swarm algorithm (CQGAPSO) is applied to tune both structure and parameters of a feedforward neural network (NN) simultaneously. In CQGAPSO algorithm, QGA is used to optimize the network structure and PSO algorithm is employed to search the parameters space. The amplitude-based coding method and cooperation mechanism improve the learning efficiency, approximation accuracy and generalization of NN. Furthermore, the ill effects of approximation ability caused by redundant structure of NN are eliminated by CQGAPSO. The experimental results show that the proposed method has better prediction accuracy and robustness in forecasting the sunspot numbers problems than other training algorithms in the literatures.

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