By Topic

A Novel Algorithm for Fault Diagnosis of Analog Circuit with Tolerances Using Improved Binary-tree SVMs Based on SOMNN Clustering

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
$33 $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

5 Author(s)
Anna Wang ; Northeastern University, China ; Junfang Liu ; Hua Li ; Feng Luan
more authors

In order to solving fault diagnosis of analog circuit with tolerances, noise, circuit nonlinearities and small sample sets, a novel multi-class classification algorithm which combined binary tree SVMs multi-classification based on self-organizing map nerve network (SOMNN) clustering roughly was proposed. The robustness characteristic of SOMNN based on the separability between pattern classes and support vector machine (SVM) based on the theory of statistic learning for the small sample set were integrated in the algorithm. The SOMNN was firstly applied to cluster layer by layer, by which structure of binary-tree SVMs multi-classifier for fault diagnosis was established, namely, the fault classes at each node of the tree were nailed down. Then according to the preprocess results of SOMNN, SVM were utilized to segment each decision node accurately. The simulation results show us that compared with the several existent multi-class classification methods, the current algorithm has high accuracy and speed.

Published in:

Third International Conference on Natural Computation (ICNC 2007)  (Volume:1 )

Date of Conference:

24-27 Aug. 2007