Browse by author
Lookup NU author(s): Professor Cheng Chin,
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
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.
Author(s): Chin C, Ji X
Publication type: Article
Publication status: Published
Journal: Engineering Applications of Artificial Intelligence
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
Altmetrics provided by Altmetric