By Topic

Quality Assessment Based on Particle Swarm and Normal Similarity

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Tie Wang ; Sch. of Vehicle, Shenyang Ligong Univ., Shenyang ; Gaonan Wang ; Zhiguang Chen ; Jianyang Lin

To assess quality fast and accurate, analyze the K-means clustering, point out that the main advantages of k-means algorithm are its simplicity and speed which allows it to run on large datasets .Introduce the method of particle swarm optimization, through calculation, point out that all the particles are likely to faster convergence on the optimal solution. According to the character of quality assessment that mean and standard deviation are considered, supply a normal similarity method; Result: The method that combines particle swarm optimization with normal similarity to assess quality is feasible.

Published in:

Future Information Technology and Management Engineering, 2008. FITME '08. International Seminar on

Date of Conference:

20-20 Nov. 2008