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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/41797

Title: Test case based risk predictions using neural network
Authors: S.T. Ung;V. Williams;S. Bonsall;J. Wang
Contributors: 國立臺灣海洋大學:商船學系
Keywords: Risk assessment;Fuzzy set theory;Fuzzy rule base;Artificial neural network;Navigational safety
Date: 2006-02
Issue Date: 2017-03-28T01:56:44Z
Publisher: Journal of Safety Research
Abstract: Introduction

The traditional fuzzy-rule-based risk assessment technique has been applied in many industries due to the capability of combining different parameters to obtain an overall risk. However, a drawback occurs as the technique is applied in circumstances where there are multiple parameters to be evaluated that are described by multiple linguistic terms.

Method

In this study, a risk prediction model incorporating fuzzy set theory and Artificial Neural Network (ANN) capable of resolving the problem encountered is proposed. An algorithm capable of converting the risk-related parameters and the overall risk level from the fuzzy property to the crisp-valued attribute is also developed. Its application is demonstrated by a test case evaluating the navigational safety within port areas.

Results

It is concluded that a risk predicting ANN model is capable of generating reliable results as long as the training data takes into account any potential circumstance that may be met.

Impact on industry

This paper provides safety assessment practitioners with a novel and flexible framework of modelling risks using a fuzzy-rule-base technique. It is especially applicable in circumstances where there are multiple parameters to be considered. The proposed framework also enables the port industry to manage navigational safety in a rational manner.
Relation: 37(3) pp.245-260
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/41797
Appears in Collections:[商船學系] 期刊論文

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