Modified multiple generalized regression neural network models using fuzzy C-means with principal component analysis for noise prediction of offshore platform

Chin, Cheng Siong, Ji, Xi, Woo, Wai Lok, Kwee, Tiaw Joo and Yang, Wenxian (2019) Modified multiple generalized regression neural network models using fuzzy C-means with principal component analysis for noise prediction of offshore platform. Neural Computing and Applications, 31 (4). pp. 1127-1142. ISSN 0941-0643

[img]
Preview
Text (Final published version)
Chin2019_Article_ModifiedMultipleGeneralizedReg.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (1MB) | Preview
[img]
Preview
Text (Advance online version)
Chin2017_Article_ModifiedMultipleGeneralizedReg.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (1MB) | Preview
Official URL: https://doi.org/10.1007/s00521-017-3143-0

Abstract

A modified multiple generalized regression neural network (GRNN) is proposed to predict the noise level of various compartments onboard of the offshore platform. With limited samples available during the initial design stage, GRNN can cause errors when it maps the available inputs to sound pressure level for the entire offshore platform. To obtain more relevant group for GRNNs training, fuzzy C-mean (FCM) is used. However, outliers in some group may interfere the prediction accuracy. The problem of selecting suitable inputs parameters (in each cluster) is often impeded by lack of accurate information. Principal component analysis (PCA) is used to ensure high relevance input variables in each cluster. By fusing multiple GRNNs by an optimal spread parameter, the proposed modeling scheme becomes quite effective for modeling multiple frequency-dependent data set (ranging from 125 to 8000 Hz) with different input parameters. The performance of FCM-PCA-GRNNs has improved significantly as the results show a 25% improvement on the spatial sound pressure level (SPL) and 85% improvement on the spatial average SPL than just GRNNs alone. By comparing with data obtained from real engine room on a jack-up rig, the FCM-PCA-GRNNs noise model performs better with around 16% less error than the empirical-based acoustic models. Additionally, the results show comparable performance to statistical energy analysis that requires more time and resources to solve during the early stage of the offshore platform design.

Item Type: Article
Uncontrolled Keywords: Fuzzy C-mean, Principal component analysis, Generalized regression neural network, Noise prediction, Offshore platform
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 03 Apr 2019 11:13
Last Modified: 29 Apr 2019 12:30
URI: http://nrl.northumbria.ac.uk/id/eprint/38719

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence