Chaos-enhanced Stochastic Fractal Search algorithm for Global Optimization with Application to Fault Diagnosis

Autor:	N. A. Abdul Jalil, Azizan As’arry, Raja Mohd Kamil Raja Ahmad, Tuan A. Z. Rahman
Rok vydání:	2017
Předmět:	business.industry Chaotic Machine learning computer.software_genre Vibration Support vector machine Fractal Search algorithm Ball (bearing) Stochastic optimization Artificial intelligence business computer Global optimization Algorithm Mathematics
Zdroj:	IOP Conference Series: Materials Science and Engineering. 210:012060
ISSN:	1757-899X 1757-8981
DOI:	10.1088/1757-899x/210/1/012060
Popis:	Support vector machine (SVM) has been known as one-state-of-the-art pattern recognition method. However, the SVM performance is particularly influenced byits parameter selection. This paper presents the parameter optimization of an SVM classifier using chaos-enhanced stochastic fractal search (SFS) algorithm to classify conditions of a ball bearing. The vibration data for normal and damaged conditions of the ball bearing system obtained from the Case Western Reserve University Bearing Data Centre. Features based on time and frequency domains were generated to characterize the ball bearing conditions. The performance of chaos-enhanced SFS algorithms in comparison to their predecessor algorithm is evaluated. In conclusion, the injection of chaotic maps into SFS algorithm improved its convergence speed and searching accuracy based on the statistical results of CEC 2015 benchmark test suites and their application to ball bearing fault diagnosis.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f8fc269ddd27f4b8eccb56ba807a3ca5 https://doi.org/10.1088/1757-899x/210/1/012060 Zobrazit plný text záznamu