Please use this identifier to cite or link to this item:
|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|
|Keywords: ||risk assessment;fuzzy set theory;fuzzy rule base;artificial neural network;navigational safet|
|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|
|Appears in Collections:||[商船學系] 期刊論文|
Files in This Item:
All items in NTOUR are protected by copyright, with all rights reserved.