SVM binary tree multi-classification method based on IBQPSO and its application in analog circuits fault diagnosis

2016 ◽  
Author(s):  
Yang Sen ◽  
Song Xiao
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Binary Tree ◽  
Classification Method ◽  
Multi Classification

scholarly journals A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

2016 ◽  
Vol 28 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Hongzhuan Zhao ◽  
Dihua Sun ◽  
Min Zhao ◽  
Senlin Cheng
Keyword(s):  
Support Vector Machine ◽  
Information Fusion ◽  
Binary Tree ◽  
Classification Method ◽  
Traffic Information ◽  
Support Vector ◽  
Cyber Physical System ◽  
Svm Classification ◽  
Forecasting Performance ◽  
Multi Classification

With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS). Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM) classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.

Analog Circuits Fault Diagnosis Using Multifractal Analysis

2013 ◽  
Vol 721 ◽  
pp. 367-371
Author(s):  
Yong Kui Sun ◽  
Zhi Bin Yu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Analog Circuits ◽  
Multifractal Analysis ◽  
Multifractal Spectrum ◽  
Experimental Results ◽  
Support Vector ◽  
Diagnosis Accuracy ◽  
Circuit Under Test

Analog circuits fault diagnosis using multifractal analysis is presented in this paper. The faulty response of circuit under test is analyzed by multifratal formalism, and the fault feature consists of multifractal spectrum parameters. Support vector machine is used to identify the faults. Experimental results prove the proposed method is effective and the diagnosis accuracy reaches 98%.

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