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Adaptive Online Sequential Extreme Learning Machine for Frequency-Dependent Noise Data on Offshore Oil Rig

Lookup NU author(s): Professor Cheng Chin, Xi Ji

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Abstract

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

Publication metadata

Author(s): Chin C, Ji X

Publication type: Article

Publication status: Published

Journal: Engineering Applications of Artificial Intelligence

Year: 2018

Volume: 74

Pages: 226-241

Print publication date: 01/09/2018

Online publication date: 06/07/2018

Acceptance date: 25/06/2018

ISSN (print): 0952-1976

ISSN (electronic): 1873-6769

Publisher: Elsevier

URL: https://doi.org/10.1016/j.engappai.2018.06.010

DOI: 10.1016/j.engappai.2018.06.010

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