Abstract
Kernel entropy component analysis(KECA) is a new method for data transformation and dimensionality reduction. However it is sensitive to a single kernel radius. By analysis of the relation of statistics in the kernel feature space, improved KECA introduces two kernel radii and an adjusting factor to make KECA less sensitive to kernel radius. A method for fault diagnosis of analog circuits based on the combination of improved KECA and minimum variance extreme learning machine(ELM)is presented. Through wavelet decomposition of sampled signals, features are extracted. Improved KECA for feature dimension reduction is used. Then the fault patterns are classified by minimum variance ELM. Case studies on two analog circuits demonstrating our diagnostics method are presented.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
