Adaptive online sequential extreme learning machine for frequency-dependent noise data on offshore oil rig. (September 2018)

• Record Type: Journal Article
• Authors: Chin, Cheng Siong; Ji, Xi
• Is Part Of: Engineering applications of artificial intelligence. Volume 74(2018)
• Page Start: 226
• Page End: 241
• Publication Date: 2018-09
• Subjects: Multiple frequency dependent data -- Extreme learning machine -- Oil-rig -- Noise prediction -- Training time -- Root mean square error; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.06.010
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: An adaptive online sequential extreme learning machine (AOS-ELM) is proposed to predict the frequency-dependent sound pressure level (SPL) data of various compartments onboard of the offshore platform. With limited samples and sequential data for training during the initial design stage, conventional neural network training gives significant errors and long computing time when it maps the available inputs to sound pressure level for the entire offshore platform. By using AOS-ELM, it allows a gradual increase in the dataset that is hard to obtain during the initial design stage of the offshore platform. The SPL prediction using AOS-ELM has improved with smaller root mean squared error in testing and shorter training time as compared with other types of ELM based learnings and other gradient based methods in neural network training.