scholarly journals Operational Risk Management Using a Fuzzy Logic Inference System

2009 ◽  
Author(s):  
Carlos León
Keyword(s):  
Risk Management ◽  
Fuzzy Logic ◽  
Operational Risk ◽  
Inference System ◽  
Operational Risk Management ◽  
Fuzzy Logic Inference ◽  
Logic Inference

scholarly journals Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment Loads

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1481 ◽  
Author(s):  
Waqas Hussan ◽  
Muhammad Khurram Shahzad ◽  
Frank Seidel ◽  
Franz Nestmann
Keyword(s):  
Fuzzy Logic ◽  
Snow Cover ◽  
Sediment Load ◽  
Snow Cover Area ◽  
Inference System ◽  
Sediment Yields ◽  
Fuzzy Logic Inference ◽  
Sediment Loads ◽  
Neuro Fuzzy ◽  
Logic Inference

The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R2 value of 0.85 and 0.74 during the training and testing period, respectively.

scholarly journals Prioritizing facilities linked to corporate strategic objectives using a fuzzy model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Devin DePalmer ◽  
Steven Schuldt ◽  
Justin Delorit
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Portfolio Management ◽  
Traditional Approach ◽  
Model Performance ◽  
Inference System ◽  
Content Type ◽  
Strategic Objectives ◽  
Fuzzy Logic Inference ◽  
Logic Inference

Purpose Limited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues. Design/methodology/approach A Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion. Findings Results of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value. Originality/value This paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.

scholarly journals Two-Stage Fuzzy Logic Inference Algorithm for Maximizing the Quality of Performance under the Operational Constraints of Power Grid in Electric Vehicle Parking Lots

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4634
Author(s):  
Shahid Hussain ◽  
Ki-Beom Lee ◽  
Mohamed A. Ahmed ◽  
Barry Hayes ◽  
Young-Chon Kim
Keyword(s):  
Fuzzy Logic ◽  
Distribution System ◽  
Power Grid ◽  
Two Stage ◽  
Inference System ◽  
Fuzzy Logic Inference ◽  
Parking Lot ◽  
Operational Constraints ◽  
Logic Inference

The widespread adoption of electric vehicles (EVs) has entailed the need for the parking lot operators to satisfy the charging and discharging requirements of all the EV owners during their parking duration. Meanwhile, the operational constraints of the power grids limit the amount of simultaneous charging and discharging of all EVs. This affects the EV owner’s quality of experience (QoE) and thereby reducing the quality of performance (QoP) for the parking lot operators. The QoE represents a certain percentage of the EV battery required for its next trip distance; whereas, the QoP refers to the ratio of EVs with satisfied QoE to the total number of EVs during the operational hours of the parking lot. This paper proposes a two-stage fuzzy logic inference based algorithm (TSFLIA) to schedule the charging and discharging operations of EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The first stage fuzzy inference system (FIS) of TSFLIA is modeled based on the real-time arrival and departure probability density functions in order to calculate the aggregated charging and discharging energies of EVs according to their next trip distances. The second stage FIS evaluates several dynamic and uncertain input parameters from the electric grid and from EVs to distribute the aggregated energy among the EVs by controlling their charging and discharging operations through preference variables. The feasibility and effectiveness of the proposed algorithm are demonstrated through the IEEE 34-node distribution system.

Improving risk assessment model of cyber security using fuzzy logic inference system

2018 ◽  
Vol 74 ◽  
pp. 323-339 ◽  
Author(s):  
Mansour Alali ◽  
Ahmad Almogren ◽  
Mohammad Mehedi Hassan ◽  
Iehab A.L. Rassan ◽  
Md Zakirul Alam Bhuiyan
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Cyber Security ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Inference System ◽  
Fuzzy Logic Inference ◽  
Logic Inference

scholarly journals Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system

2017 ◽  
Vol 42 ◽  
pp. 393-410 ◽  
Author(s):  
Mohamed-Ayoub Messous ◽  
Hichem Sedjelmaci ◽  
Sidi-Mohammed Senouci
Keyword(s):  
Fuzzy Logic ◽  
Mobility Model ◽  
Inference System ◽  
Fuzzy Logic Inference ◽  
Logic Inference
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