Toggle Main Menu Toggle Search

Open Access padlockePrints

Modified Multiple Generalized Regression Neural Network Models using Fuzzy C-Means with Principal Component Analysis for Noise Prediction of Offshore Platform

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.

Publication metadata

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


DOI: 10.1007/s00521-017-3143-0


Altmetrics provided by Altmetric


Funder referenceFunder name