Title :
Application of IWO-SVM approach in fault diagnosis of analog circuits
Author :
Shuxiang Cai ; Haiwen Yuan ; Jianxun Lv ; Yong Cui
Author_Institution :
Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing, China
Abstract :
Support vector machine (SVM) is a machine learning algorithm which has been applied to fault diagnosis of analog circuits. Invasive weed optimization (IWO) is a novel numerical optimization algorithm inspired from weed colonization. An approach that combines IWO and SVM (IWO-SVM) is proposed to fault diagnosis of analog circuits in this paper. The process of fault diagnosis of analog circuits using IWO-SVM approach is introduced in details. A biquadrate filter is used to test the performance of IWO-SVM approach for fault diagnosis. The simulation experiments show that the IWO-SVM approach proposed in this paper has a higher diagnosis accuracy rate than the conventional SVM in fault diagnosis of analog circuits.
Keywords :
analogue circuits; biquadratic filters; electronic engineering computing; learning (artificial intelligence); numerical analysis; optimisation; support vector machines; IWO-SVM approach; analog circuits; biquadrate filter; fault diagnosis; invasive weed optimization; machine learning algorithm; numerical optimization algorithm; support vector machine; weed colonization; Accuracy; Analog circuits; Circuit faults; Fault diagnosis; Optimization; Support vector machines; Training; Analog Circuits; Fault Diagnosis; IWO; IWO-SVM; SVM;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561800