An interval support vector domain description based on the dynamic decreasing inertia weight particle swarm optimization. (3rd December 2019)

• Record Type: Journal Article
• Authors: Guo, Chengjun; Chen, Yongqi
• Other Names: Xu Zheng guestEditor.; Zhou Qingyuan guestEditor.; Dong Fang guestEditor.; Yong Jianming guestEditor.; Fei Xiang guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 23(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-03
• Subjects: fault diagnosis -- interval -- parameters selection -- rotor crack; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5591
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: For the fault diagnosis of the interval rotor crack fault samples, this paper proposes an interval support vector domain description method through using the dynamic decreasing inertia weight particle swarm optimization (DDIWPSO). Firstly, the interval Gauss function is constructed by using the interval midpoint and interval radius. Applying the interval Gauss kernel function as the kernel, the interval support vector domain description is proposed and can realize the classification of interval samples. Secondly, the DDIWPSO is applied to select the optimal penalty parameter C, the Gauss interval kernel width parameter σ, and the factor λ of the proposed method. Finally, interval University of California Irvine (UCI) samples and the interval rotor crack data are used to verify the advantages of this method. The experimental verification shows that the interval support vector domain description method has the higher accuracy compared with the traditional interval fault classification methods because this method owns the empirical risk minimization of SVM and the optimal parameters selection based on the DDIWPSO.