Fabric wrinkle level classification via online sequential extreme learning machine based on improved sine cosine algorithm. Issue 17 (September 2020)

• Record Type: Journal Article
• Authors: Zhou, Zhiyu; Zhang, Ruoxi; Zhang, Jianxin; Wang, Yaming; Zhu, Zefei; Liu, Chengxia
• Is Part Of: Textile research journal. Volume 90:Issue 17/18(2020)
• Page Start: 2007
• Page End: 2021
• Publication Date: 2020-09
• Subjects: wrinkle level classification -- online sequential extreme learning machine -- sine cosine algorithm -- differential evolution; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/0040517520908072
• Languages: English
• ISSNs: 0040-5175

Abstract

Because it is difficulty to classify level of fabric wrinkle, this paper proposes a fabric winkle level classification model via online sequential extreme learning machine based on improved sine cosine algorithm (SCA). The SCA has excellent global optimization ability, can explore different search spaces, and effectively avoid falling into local optimum. Because the initial population of SCA will have an impact on its optimization speed and quality, the SCA is initialized by differential evolution (DE) to avoid local optimization, and then the output weight and hidden layer bias are optimized; that is, the improved SCA is used to select the optimal parameters of the online sequential extreme learning machine (OSELM) to improve the generalization performance of the algorithm. To verify the performance of the proposed model DE-SCA-OSELM, it will be compared with other algorithms using a fabric wrinkles dataset collected under standard conditions. The experimental results indicate that the proposed model can effectively find the optimal parameter value of OSELM. The average classification accuracy increased by 6.95%, 3.62%, 6.67%, and 3.34%, respectively, compared with the partial algorithms OSELM, SCAELM, RVFL and PSOSVM, which meets expectations.