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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/25818

Title: Test case based risk predictions using artificial neural network
Authors: S.T. Ung;V. Williams;S. Bonsall;J. Wang
Contributors: NTOU:Department of Merchant Marine
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 safet
Date: 2006
Issue Date: 2011-10-20T08:32:32Z
Publisher: Journal of Safety Research
Abstract: 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/handle/987654321/25818
Appears in Collections:[商船學系] 期刊論文

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