A hybrid fuzzy-probabilistic system for risk analysis in petroleum exploration prospects

Roisenberg, Mauro; Schoeninger, Cíntia; Silva, Reneu Rodrigues da

doi:10.1016/j.eswa.2008.07.060

Cited by 35 publications

(15 citation statements)

References 20 publications

(21 reference statements)

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“…However, most discussions in literatures of risk management (Pennings & Smidts, 2000;Iversen, Mathiassen, & Nielson, 2004;Benaroch, Lichtenstein, & Robinson, 2006;Dewan & Ren, 2007;Gefen, Wyss, & Lichtenstein, 2008;Berkowits, Christoffersen, & Pelletier, 2009;Lin & Ko, 2009;Roisenberg, Schoeninger, & da Silva, 2009) do not seem to generalize to present a systematical way of designing such a SSS.…”

Section: Model Risk and System Flexibility Requirementmentioning

confidence: 93%

A systematic design for coping with model risk

Yang

Tsaih

Hsieh

2011

Expert Systems with Applications

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Section: Model Risk and System Flexibility Requirementmentioning

confidence: 93%

A systematic design for coping with model risk

Yang

Tsaih

Hsieh

2011

Expert Systems with Applications

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“…A fuzzy rule-based system is formed entirely linguistic rule base. The rule base and the data base both represent the knowledge base of the Mamdani fuzzy rule-based system [20].…”

Section: B Mamdani Fuzzy Inference Systemmentioning

confidence: 99%

A Real-Time Robust Fuzzy-based Level Control using Programmable Logic Controller

Aydoğmuş

2015

ElAEE

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Industrial software are specially designed to meet specific process. And sometimes this software can be very expensive. One way to reduce this cost is to develop own algorithm. This paper presents a real-time liquid level control implementation for an industrial process. The implementation of the level control is achieved using a fuzzy-based control algorithm. A Programmable Logic Controller (PLC) was used in which the fuzzy control algorithm has been embedded and can withstand against abrupt changes in the outlet flow. In addition, the fuzzy control algorithm is introduced to the system without a fuzzy module or software package. As a consequence, a robust and low cost liquid level control system has been realized. A Mamdani Fuzzy Inference System (MFIS) has been proposed. Membership functions of the input and output variables of the system were obtained and verified using Matlab/Simulink environment. The obtained functions have been used for the construction of fuzzy control algorithm in PLC. A robust fuzzy-based level controller has been achieved and quite satisfactory results were obtained in real-time implementation for different outlet flow rates.

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“…The process of FRBS is started from a given input to output using a set of fuzzy ifthen linguistic rules which are generated from FGDC, whose antecedents and consequents are compound of fuzzy statements, related by the concepts of fuzzy implication and the compositional rule of inference [9]. The fuzzy reasoning is applied to determine the final classes of collision events in four steps.…”

Section: Reasoning With Fuzzy Granular Decision Treementioning

confidence: 99%

Vehicle Accident Severity Rules Mining Using Fuzzy Granular Decision Tree

Moghaddam

Wang

2014

Lecture Notes in Computer Science

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Road accident is a disaster that vocalizes a major cause of disability, untimely death and the loss of human lives. Therefore, investigating the condition of road accidents for prediction and prevention purposes on highways is significant. In this paper, we propose a new fuzzy granular decision tree to generate the road accident rules applying the discrete and continuous data stored in accident databases. Among all critical factors in the occurrence of traffic accidents, environmental factors and road design (geometry) are considered in this study. This method establishes an optimized fuzzy granular decision tree with the minimum redundancy and road accident severity classification using fuzzy reasoning. California highways were considered as the case study to examine the proposed approach. The experimental results demonstrate that the proposed method is approximately 16% more accurate than the fuzzy ID3 method with less redundancy in constructing the decision tree.

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A hybrid fuzzy-probabilistic system for risk analysis in petroleum exploration prospects

Cited by 35 publications

References 20 publications

A systematic design for coping with model risk

A systematic design for coping with model risk

A Real-Time Robust Fuzzy-based Level Control using Programmable Logic Controller

Vehicle Accident Severity Rules Mining Using Fuzzy Granular Decision Tree

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