Fuzzy Framework Research Articles

Development of a comprehensive fuzzy based approach for evaluating sustainability and self-purifying capacity of river Ganges

With accelerated and uncontrolled developments, large amount of untreated wastes is discharged into river water courses through various open drains. Though rivers possess self-purifying capacity, water withdrawals for different beneficial uses have impacted it significantly by reducing its flow. Presently, sustainability has also become an important affair of river basin planning and management. Therefore, assessment of behavior of river under sustainability criteria is necessary. However, the uncertainty and complexity associated with the sustainability criteria, randomness of hydrologic variables, decision-makers, and missing data have become a concern for water managers. Such problems can be modeled under fuzzy logic framework. The present work develops a comprehensive artificial intelligence approach, namely ‘MATLAB Fuzzy Inference system’ to determine the self-purifying capacity of the River Ganges. Thirty-three wastewater drains are identified, which discharge untreated wastes along Kanpur–Varanasi stretch of Ganges. Critical water quality parameters have been analyzed and impact of discharge of river at 12 sampling stations is studied. The model developed to measure the sustainability is flexible to incorporate spatial/temporal changes. Final results give emphatic information to water authorities to maintain adequate flow in the river needed to dilute the waste and also in determining the treatment technology and capacity for open drains.

A two-stage fuzzy multi-objective framework for segmentation of 3D MRI brain image data

Display Omitted A novel two-stage fuzzy multi-objective framework (2sFMoF) has been proposed for segmenting 3D MRI brain image data.The first stage includes 3D spatial neighbourhood information and...

A new fuzzy framework for the optimal placement of phasor measurement units under normal and abnormal conditions

This paper presents a new procedure for finding the optimal placement of the phasor measurement units(PMUs) in modern power grids to achieve full network observability under normal operating conditions,and also abnormal operating conditions such as a single line or PMU outage, while considering the availabilityof PMU measuring channels. The proposed modeling framework is implemented using the fuzzybinary linear programming (FBLP) technique. Linear fuzzy models are proposed for the objective functionand constraints alike. The proposed procedure is applied to five benchmark systems such as theIEEE 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus. The numerical results demonstrate that the proposedframework is capable of finding a fine-tuned optimal solution with a simple model and acceptable solutioncharacteristics compared with early works in the literature. Besides, four evaluation indices areintroduced to assure the various criteria under study such as the observability depth, measurement redundancy,and robustness of the method under contingencies. The results show that full network observabilitycan be met under normal conditions using 20% PMUs penetration; however, under contingencies,approximately 50% PMUs penetration is required. The novelty of the proposed procedure has proven thecapability of the proposed linear fuzzy models to find better optimal number of PMUs with lower numberof channels compared to other algorithms under various operating conditions. Hence, the proposedwork represents a potential tool to monitor power systems, and it will help the operators in a smart gridenvironment.

Robust Fault Detection for Switched Fuzzy Systems With Unknown Input.

This paper investigates the fault detection problem for a class of switched nonlinear systems in the T-S fuzzy framework. The unknown input is considered in the systems. A novel fault detection unknown input observer design method is proposed. Based on the proposed observer, the unknown input can be removed from the fault detection residual. The weighted performance level is considered to ensure the robustness. In addition, the weighted performance level is introduced, which can increase the sensibility of the proposed detection method. To verify the proposed scheme, a numerical simulation example and an electromechanical system simulation example are provided at the end of this paper.

A framework of fuzzy integrated MADM and GMA for maintenance strategy selection based on agile enabler attributes

BackgroundMaintenance cost of machinery, equipment and tooling are main contributor to total cost of production in processing industry. The aim of study is to explore selection of optimum maintenance strategy for steam generating unit (SGU) in a coal fired thermal power plant using various MADM and graphical approach. The novelty of research work is selection of strategy based on agile enabler attributes and use of graphical approaches for decision making. This selection procedure allows for speedy conflux of large number of attributes, which gives the decision maker an effective tool select optimum strategy.Case descriptionThe practicality of the framework has been justified through implementation in thermal power plant, situated in northern part of India. The plant has total installed capacity of 2400 MW. It has eight units. The present study is done in unit number 8.Discussion and evaluationThis selection is based on agile enabler attributes. Triangular fuzzy number (TFN) is used to take care of imprecise and vague judgements. For ranking of maintenance strategies MADM approaches of fuzzy AHP (FAHP), fuzzy TOPSIS (FTOPSIS); and Graphical approaches of Line graph and spider graph are used, respectively.ConclusionPDM has been ranked first by all evaluating approaches. There is also synergy in results. The proposed work not only provides the best alternative but also alternative ranking. The results will be shared with management for implementation so as to increase system availability and reduce operation cost.

A new fuzzy framework for the optimal placement of phasor measurement units under normal and abnormal conditions

Abstract This paper presents a new procedure for finding the optimal placement of the phasor measurement units (PMUs) in modern power grids to achieve full network observability under normal operating conditions, and also abnormal operating conditions such as a single line or PMU outage, while considering the availability of PMU measuring channels. The proposed modeling framework is implemented using the fuzzy binary linear programming (FBLP) technique. Linear fuzzy models are proposed for the objective function and constraints alike. The proposed procedure is applied to five benchmark systems such as the IEEE 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus. The numerical results demonstrate that the proposed framework is capable of finding a fine-tuned optimal solution with a simple model and acceptable solution characteristics compared with early works in the literature. Besides, four evaluation indices are introduced to assure the various criteria under study such as the observability depth, measurement redundancy, and robustness of the method under contingencies. The results show that full network observability can be met under normal conditions using 20% PMUs penetration; however, under contingencies, approximately 50% PMUs penetration is required. The novelty of the proposed procedure has proven the capability of the proposed linear fuzzy models to find better optimal number of PMUs with lower number of channels compared to other algorithms under various operating conditions. Hence, the proposed work represents a potential tool to monitor power systems, and it will help the operators in a smart grid environment.

New definitions of mean value and variance of fuzzy numbers: An application to the pricing of life insurance policies and real options

We propose a new definition of mean value and variance for fuzzy numbers whose membership functions are upper-semicontinuous but are not necessarily continuous. Our proposal uses the total variation of bounded variation functions. The proposed concepts are used for the evaluation of insurance contracts and real options in a fuzzy framework.

Fuzzy decision-making framework for treatment selection based on the combined QUALIFLEX–TODIM method

ABSTRACTTreatment selection is a multi-criteria decision-making problem of significant concern in the medical field. In this study, a fuzzy decision-making framework is established for treatment selection. The framework mitigates information loss by introducing single-valued trapezoidal neutrosophic numbers to denote evaluation information. Treatment selection has multiple criteria that remarkably exceed the alternatives. In consideration of this characteristic, the framework utilises the idea of the qualitative flexible multiple criteria method. Furthermore, it considers the risk-averse behaviour of a decision maker by employing a concordance index based on TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method. A sensitivity analysis is performed to illustrate the robustness of the framework. Finally, a comparative analysis is conducted to compare the framework with several extant methods. Results indicate the advantages of the framework and its better performance compared with the extant methods.

Multi-granulation bipolar-valued fuzzy probabilistic rough sets and their corresponding three-way decisions over two universes

This article introduces general framework of multi-granulation bipolar-valued fuzzy (BVF) probabilistic rough sets (MG-BVF-PRSs) models in multi-granulation BVF probabilistic approximation space over two universes. Four types of MG-BVF-PRSs are established, by the four different conditional probabilities of BVF event. For different constraints on parameters, we obtain four kinds of each type MG-BVF-PRSs over two universes. To find a suitable way of explaining and determining these parameters in each kind of each type MG-BVF-PRS, three-way decisions (3WDs) are studied based on Bayesian minimum-risk procedure, i.e., the multi-granulation BVF decision-theoretic rough set (MG-BVF-DTRS) approach. The main contribution of this paper is twofold. One is to extend the fuzzy probabilistic rough set (FPRS) to MG-BVF-PRS model over two universes. Another is to present an approach to select parameters in MG-BVF-PRS modeling by using the process of decision making under conditions of risk.

Periodic review inventory model with normally distributed fuzzy random variable demand

ABSTRACTIn this article, a periodic review inventory model has been analysed in a fuzzy random framework. In order to incorporate the two different kind of uncertainties, namely fuzziness and randomness, that characterise any real-life inventory situation simultaneously, the model has been developed in the mixed fuzzy random environment. The amalgamation of these two uncertainties has been accomplished by assuming the annual customer demand to be a continuous fuzzy random variable following the normal distribution. The lead-time has been assumed to be constant. The lead-time demand and the lead-time plus one period's demand have also been assumed to be normally distributed fuzzy random variables by connecting them to the annual customer demand through the length of the lead-time and the lead-time plus one period's demand, respectively. An algorithm has been proposed to determine the optimal period of review and the optimal target inventory level so as to minimise the total expected annual inventory cost. The proposed methodology has been illustrated by way of a numerical example.

Evaluating sustainable supply chain indicators using fuzzy AHP

PurposeSustainability in supply chain is gaining attention in recent years due to environmental concern, enforced legislation, green issues, social responsibility, etc. Sustainable supply chain (SSC) has revolved around the various dimensions including economy, environment and societal factors since its inception. The purpose of this paper is to identify, prioritize and evaluate the indicators of SSC so that organizations can cultivate strategies to implement them on priority.Design/methodology/approachThis paper proposes a methodology based on fuzzy analytic hierarchy process to prioritize the indicators of SSC. A numerical analysis of Indian automotive industry is presented to demonstrate the use of the proposed method. This proposed method considered fuzzy framework that can handle impreciseness and uncertainty. Sensitivity analysis is also performed to test the robustness of the proposed model.FindingsPotential indicators are identified from relevant literature and validated by industry experts. This research finalizes the SSC indicators under three dimensions so that prioritization of identified indicators can be developed and the insights relationship of factors would be explored. The results of the study found that environmental and social dimensions of sustainability contribute more toward the sustainability.Research limitations/implicationsThis study is limited to identify evaluation factors and other factors have not been identified and categorized. Evaluation is done by experts in this area so it is natural that views of decision makers may be subjective and vary with regard to industry type, priorities, resources, etc.Practical implicationsThis study will help industry to identify, evaluate and prioritize factors for successful implementation of sustainability in their supply chain. Automotive companies could device these factors by applying the outcome of the study in their operations with higher priority to integrate sustainability in their supply chain.Originality/valueThese factors are identified to implement sustain ability into supply chain practices for automotive industry.

The Imperialist Competitive Algorithm for Optimal Multi-Objective Location and Sizing of DSTATCOM in Distribution Systems Considering Loads Uncertainty

The optimal location and sizing of distribution static compensator (DSTATCOM) in distribution systems is a complex nonlinear problem. This problem is constrained by various technical limits and can offer different objectives that would provide many benefits to the network. These include minimization of power losses, index of voltage profile, load balancing index, and annual cost saving index which have been considered in this paper. In the present work, the Imperialist Competitive Algorithm (ICA) is employed for optimizing the distribution systems where an optimal location and sizing of DSTATCOM is investigated. In this study, an aggregating operator named Max-geometric mean is used for combination of objectives and providing overall objective function. The scaling of objectives is performed in the fuzzy framework. The proposed algorithm is implemented in 33 and 69 buses IEEE test systems. Furthermore, the uncertainty of the loads of the balanced system is modeled by using a fuzzy technique. Based on the numerical results of this work, one can extract that the performance of the ICA is slightly higher than other meta-heuristic algorithms; hence the introduced approach can be used by utility services for optimal DSTATCOM allocation and sizing in the distribution systems.

Impact assessment of industrial wastewater discharge in a river basin using interval-valued fuzzy group decision-making and spatial approach

Sustainable and integrated river basin planning and management is a complex process involving uncertain data at different stages of decision-making process. Moreover, there are multiple decision makers at different institutions with contrasting interests and objectives, and thus, a collaborative decision making is required to resolve the conflicts. Although the formulation or modeling of such problems under fuzzy framework provides a very strong ground to deal with the uncertain and complex judgments, there is scope to model the problem more accurately. The present study develops a novel approach of dealing with uncertainty associated with group decision making in a river basin, by extending fuzzy Delphi process using interval-valued fuzzy sets. A case study of assessing the impact of industrial wastewaters on the Ganges River basin, India, has also been presented to demonstrate the effectiveness of the proposed methodology. A total of 33 industrial units, mainly paper pulp, tanneries and textiles, discharging massive quantities of wastewater in the Ganges River basin have been chosen for the analysis. These industries are rated by the expert decision makers to represent their objective judgments (and/or subjective preferences) on the basis of ten essential sets of criteria such as impact on river, impact on groundwater, critical pollutants level, impact on public health. The ratings are analyzed and aggregated using modified fuzzy decision-making approach, and industries are ranked accordingly. To enhance the decision-making process, the results are also represented spatially under GIS environment. Analysis of results clearly demonstrates the contribution of crucial indicators/criteria in ensuring the sustainable use of water resources with respect to environmental, social and economic dimensions. The results obtained are compared and validated with the recent research works and reports of pollution control boards. The study recommends several policy implementations, primarily revisal in prescribed effluent discharge standards of the industries. The model developed herein can be an efficient and productive tool for complex group decisions in water resources planning by facilitating participation and knowledge sharing among the experts.

Hybrid fuzzy charged system search algorithm based state estimation in distribution networks

This paper proposes a new hybrid charged system search (CSS) algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS) algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS) algorithm and particle swarm optimization (PSO) for feasibility of the algorithm.

Stock Picking by Probability–Possibility Approaches

This paper presents a performance evaluation of stock picking by merging several technical indicators. Several fusion operators have been proposed either in the probabilistic or in the possibilistic framework. The latter fuzzy framework has been introduced to manage the uncertain information embedded in financial time series due to human biases as studied by behavioral finance. Performances of portfolio resulting from the proposed systems are evaluated according to cumulative returns but also through a risk analysis point of view (Sharpe ratio). Two fusion mechanisms (one probabilistic, one possibilistic) aiming at discriminating common information from merged technical indicators, produce the higher portfolio performances. It also appears that selecting specific technical indicators affects the overall performances of the proposed stock picking systems. Indeed, studying the technical indicators selection through a shared/nonshared information point of view reveals that possibilistic framework is more robust to redundant sources than probabilistic framework. Effects of some parameters used in the fusion algorithms (amount of assets, window length analysis, $\ldots$ ) are also investigated. Results from all these tests clearly show the high potentiality of technical indicators fusion to improve portfolio performances. However, these first promising results have to be further inspected within wider contexts, as discussed at the end of the paper.

A Novel Framework for Shock Filter Using Partial Differential Equations

In dilation or erosion processes, a shock filter is widely used in signal enhancing or image deburring. Traditionally, sign function is employed in shock filtering for reweighting of edge-detection in images and decides whether a pixel should dilate to the local maximum or evolve to the local minimum. Some researchers replace sign function with tanh function or arctan function, trying to change the evolution tracks of the pixels when filtering is in progress. However, analysis here reveals that only function replacement does usually not work. This paper revisits first shock filters and their modifications. Then, a fuzzy shock filter is proposed after a membership function in a shock filter model is adopted to adjust the evolve rate of image pixels. The proposed filter is a parameter tuning system, which unites several formulations of shock filters into one fuzzy framework. Experimental results show that the new filter is flexible and robust and can converge fast.

Perspective of MAS in Power System via a Fuzzy Framework

Perspective of MAS in Power System via a Fuzzy Framework

Application of fuzzy logic and genetic algorithm in heart disease risk level prediction

As individuals have intrigues in their wellbeing now a days, advancement of therapeutic area application has been a standout amongst the most dynamic exploration territories. One case of the restorative area application is the identification framework for coronary illness taking into account. A weighted fuzzy standard based clinical decision support system is displayed for the conclusion of coronary illness, consequently acquiring learning from the clinical information. The proposed heart disease risk level prediction system using fuzzy and genetic for the risk forecast of heart patients comprises of two stages: (1) mechanized methodology for the era of weighted fuzzy rules and (2) building up a fuzzy principle based heart disease risk level prediction using genetic algorithm. At this point, the fuzzy framework is developed as per the weighted fuzzy standards and picked better qualities cases. In this study, a system that can capably locate the fundamentals to anticipate the risk level of patients in perspective of the given parameter about their wellbeing. The principle commitment of this study is to help a non-specialized doctors to settle on right choice about the coronary illness risk level. The framework’s execution is assessed and compared as far as rules precision concerned and the outcomes demonstrates that the framework has incredible potential in foreseeing the coronary illness risk level more precisely.

Dominance based fuzzy decision support framework for g-resilient (ecosilient) supplier selection: an empirical modelling

A novel decision support framework has been proposed herein to solve supplier selection problems by considering green as well as resiliency criteria, simultaneously. An expert team involving a finite number of Decision-Makers (DMs) is to be employed to obtain degree of importance of each of the evaluation criteria and also to assign an appropriate rating for each supplier alternative with respect to the criteria. Since subjective human judgment often bears some sort of ambiguity as well as vagueness, application of fuzzy set theory can be recommended in this context. The present work represents a dominance based decision support system in the light of fuzzy set theory which is basically a simplified version of TODIM and PROMETHEE. It explores dominance between two alternatives with respect to a particular criterion, based on which a global dominance measure is computed to facilitate ranking order of candidate suppliers. The approach proposed in this research seems straightforward which can exclude complex and tedious computational steps of TODIM as well as PROMETHEE. Application potential of the proposed dominance based fuzzy decision-making approach has been compared to that of Fuzzy-TOPSIS and Fuzzy-VIKOR. Good agreement has been observed. In addition to this, the concept of a unique performance index i.e. ‘g-resilient’ (also called ‘ecosilient’) index has been introduced to help in assessing suppliers’ overall performance extent. The work has also been extended to identify the ill (poor)-performing areas in which suppliers are lagging and seek further improvement towards enhancing g-resilient performance level.

Power loss reduction of distribution systems using BFO based optimal reconfiguration along with DG and shunt capacitor placement simultaneously in fuzzy framework

In distribution systems, network reconfiguration and capacitor placement are commonly used to diminish power losses and keep voltage profiles within acceptable limits. Moreover, the problem of DG allocation and sizing is great important. In this work, a combination of a fuzzy multi-objective approach and bacterial foraging optimization (BFO) as a meta-heuristic algorithm is used to solve the simultaneous reconfiguration and optimal sizing of DGs and shunt capacitors in a distribution system. Each objective is transferred into fuzzy domain using its membership function. Then, the overall fuzzy satisfaction function is formed and considered a fitness function inasmuch as the value of this function has to be maximized to gain the optimal solution. The numerical results show that the presented algorithm improves the performance much more than other meta-heuristic algorithms. Simulation results found that simultaneous reconfiguration with DG and shunt capacitors allocation (case 5) has 77.41%, 42.15%, and 56.14% improvements in power loss reduction, load balancing, and voltage profile indices, respectively in 33-bus test system. This result found 87.27%, 35.82%, and 54.34% improvements of mentioned indices respectively for 69-bus system.