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Lookup NU author(s): Professor Cheng Chin, Xi Ji, Dr Wai Lok Woo, Dr Wenxian YangORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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) are 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 dataset (ranging from 125 Hz 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 (SEA) that requires more time and resources to solve during the early stage of the offshore platform design.
Author(s): Chin CS, Ji X, Woo WL, Kwee TJ, Yang WX
Publication type: Article
Publication status: Published
Journal: Neural Computing and Applications
Year: 2019
Volume: 31
Issue: 4
Pages: 1127–1142
Print publication date: 01/04/2019
Online publication date: 12/07/2017
Acceptance date: 28/06/2017
Date deposited: 21/07/2017
ISSN (print): 0941-0643
ISSN (electronic): 1433-3058
Publisher: Springer
URL: https://doi.org/10.1007/s00521-017-3143-0
DOI: 10.1007/s00521-017-3143-0
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