Title :
Analog Circuit Fault Diagnosis Based on DE OS-ELM
Author :
Shaowei Chen ; Minhua Wu ; Shuai Zhao
Author_Institution :
Northwestern Polytech. Univ., Xi´an, China
Abstract :
Extreme Learning Machine has the quality of fast learning speed, good generalization performance, and high diagnostic accuracy. For analog circuit fault diagnosis and health management (PHM) applications, this paper presents the method of online sequential learning machine with differential evolution algorithm to optimize Extreme Learning Machine and improve the diagnostic accuracy and generalization performance effectively.
Keywords :
analogue circuits; electronic engineering computing; evolutionary computation; fault diagnosis; generalisation (artificial intelligence); learning (artificial intelligence); DE OS-ELM; PHM application; analog circuit fault diagnosis; diagnostic accuracy; evolution algorithm; extreme learning machine; generalization performance; online sequential learning machine; prognostics and health management; Analog circuits; Circuit faults; Fault diagnosis; Optimization; Sociology; Statistics; Training; analog circuits; differential evolution algorithm; online sequential learning machine;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on
Print_ISBN :
978-1-4799-7004-9
DOI :
10.1109/ISCID.2014.94
