Fuzzy Methodology Research Articles

Active fault-tolerant control system for a swash mass helicopter using back-stepping approach

In this article, an active fault-tolerant control system is designed and analyzed for a new model structure of unmanned aerial vehicles, which is referred to a swash mass helicopter. It consists of a double-blade coaxial shaft rotor for generating the necessary thrust and four masses that are located on the main body structure to maneuver the helicopter. The translational and rotational subsystems of the helicopter are highly coupled. Therefore, change of coordinate and feedback linearization methodologies are applied to address the problem and achieve the canonical form of the model. Next, actuator fault as a loss of effectiveness and bias faults are modeled for the dynamic. The main contribution of this article is to design an active fault-tolerant control system based on back-stepping and T-S fuzzy estimation methods for the swash mass helicopter which has never been designed in the last decades. T-S fuzzy methodology is responsible for obtaining the estimated value of the actuator faults during the flight. The Lyapunov theory illustrates that the proposed control strategy can stabilize the system despite the actuator’s fault. The simulation results show the effectiveness of the proposed scheme compared with another method.

Evaluation of software development projects based on integrated Pythagorean fuzzy methodology

Implementing ERP (Enterprise Resource Planning) system successfully comes with a set of costly and complex challenges. The purpose of this paper is to offer an effective cloud-based ERP software evaluation framework to gain a better understanding of these complexities and address such challenges by identifying the selection criteria. The PFS (Pythagorean Fuzzy Set) is the new extension of Intuitionistic fuzzy sets, the Simple Additive Weighting (SAW) is an easy-to-use technique to determine the weights of the criteria, and the VIKOR technique is a compromise ranking methodology. In the recommended novel integrated MCDM (Multi-Criteria Decision-Making) methodology, Decision-Makers (DMs) must choose the best one among a set of candidate alternatives that satisfies the evaluation criteria. The GDM (Group Decision Making) is preferred here to deal with hesitancy in the evaluation of alternatives under group preferences. The sensitivity analysis resulted from various scenarios reveals that the proposed method is quite sensitive to criteria weights. The service and support, cost, and functionality are found as the essential evaluation criteria for the Turkish firm. These findings emphasize the significance of a qualified group of DMs for the evaluation process. The case study findings demonstrate that the developed approach is practical and validated for solving software development projects. This study presents a novel integrated methodology based on the PFS, SAW, and VIKOR to measure the performance of software development projects, to deal with the ambiguity and imprecision caused by the subjective judgments of DMs. The research contributes to the literature by delivering a combined decision methodology for the first time and by applying it to evaluate the software development projects.

UAV and Soft Computing Methodology for Monitoring Landslide Areas (Susceptibility to Landslides and Early Warning)

In this work, we created a map of the susceptibility to landslides in GIS environment using neural network, Analytical Hierarchy Process (AHP) multicriteria analysis method and fuzzy methodology, producing five categories (levels) of risk. Subsequently, starting from this map, we identified (fuzzy methodology) the areas of the road’s network most exposed to landslide risk also using remote sensing techniques (classification and segmentation techniques) overlapped on the street map. This system therefore provides us the level of attention that affects the transport infrastructure investigated (a higher level of attention corresponds to a higher level of landslide risk). Once the risk map for a large area was identified, we focused on local monitoring of a part of it automatically selected by the GIS. The monitoring of this area was carried out through an innovative system (made by us) that allows to monitor landslide risk areas and to study landslide phenomena through the use of Unmanned Aerial Vehicles (UAVs). Specifically, with this innovative solution, data are acquired thanks to an automated system of UAVs and wireless charging platforms (capable to acquired, to transmit and to store data); subsequently, the acquired data are stored automatically in a special platform that allows us to create the point cloud and 3D models of the investigated area (which in turn they are superimposed on the digital models created in previous monitoring), also allowing the creation of the land mass displacement’s sequence in a video. Finally, in relation to early warning, the system allows civil protection to be warned in the event of a landslide risk (start of new landslides or continuation of landslides that have already begun) which in this way will be able to warn the population also through social media.

The Fuzzy Methodology’s Digitalization of the Biological Assets Evaluation in Agricultural Enterprises in Accordance with the IFRS

The study of the assessment and reflection of biological assets in the economic processes of agricultural enterprises can be represented as a chain of phenomena in which scientists and practitioners try to study and understand the nature and essence of biological assets in various aspects. This article discusses the principles of accounting for biological assets in the agricultural enterprises’ economic life of the Republic of Tajikistan and identifies the reasons and mechanisms for their reflection based on the principles of the International Financial Reporting System (IFRS) in refraction to the specifics and national features of accounting. The author gives his own interpretation of these approaches and constructs the architecture of the information module for accounting for biological assets and the results of biotransformation, based on the web services architecture (WSA) within the framework of the development trend of these and new accounting models in connection with the transition of the world economy to a digital format. The article provides specific authors’ approaches to implementing this architecture in PHP (Hypertext Preprocessor) and also implements one of the approaches to assessing the value of biological assets, based on the theory of fuzzy sets, taking into account the risk of investment inefficiency.

Enhancement of Transient Stability in a Grid-Connected Wind Power Plant Utilizing PI-FL Controller

This paper proposed a blend of corresponding PIC and FLC for controlling the pitch angle of wind turbine connected to grid. Initially, conventional control methodology is employed to design the PI controller and later on, the concept of fuzzy logic methodology is adapted to analyze the gains of proportional integral controller. Due to versatility of fuzzy logic controller and rugged structure, sturdy nature of proportional integral controller are clubbed together, it provides a virtuous stagnant and dynamic outcomes. The proposed novel technique can be assessed by considering various disturbances such as short circuit network fault. The reenactment after effects of proposed controller is contrasted with proportional integral controller and FLC. From the findings it is shown that the proposed methodology could improve stability even the wind farm is subjected to different operating conditions. Moreover, a performance index in terms of absolute maximum deviations is defined in order to evaluate the adequacy of the proposed controller.

Hybridization of an interactive fuzzy methodology with a lexicographic min-max approach for optimizing a multi-period multi-product multi-echelon sustainable closed-loop supply chain network

Here, a fuzzy multi-period multi-echelon multi-objective mixed-integer non-linear programming (MOMINLP) model is considered for a sustainable multi-product multi-site multi-distribution multi-customer supply chain in forward flow having multi-centers for collecting, checking, repairing and decomposing and multi-disposal centers in the reverse flow. Minimizing the total cost of the closed-loop supply chain (CLSC), elevating the customer satisfaction degrees, minimizing the total waiting time, minimizing the manufacturing site greenhouse gases and minimizing the CO2 emissions from vehicles are considered as the objective functions. Furthermore, integration of strategic decisions of flow allocations and vehicle routing with tactical and operational decisions such as production and workforce planning and improving upon customer satisfaction are considered. Also, we are concerned with the stability of the model and the accuracy of the obtained solution. We consider uncertainty and propose an appropriate method to develop a fair optimization of the distribution of raw materials and products to the supply chain participants. The presented solution method initially considers an adaptation of the lexicographic min–max fairness approach to finding a short delay for the delivery time of all the existing flows between every two consecutive echelons of the CLSC network. Then, a service quality measure is introduced to evaluate the uncertain delivery time considering the delay unpleasantness measure (DUM) index and measure the delay unpleasantness of all the existing delivery time delays. The model is converted to an auxiliary crisp MOMINLP problem by taking appropriate strategies, and a novel interactive fuzzy approach is proposed to find a compromised solution. The effectiveness of the algorithm is illustrated through a generated case study. The validity of the proposed method is confirmed by comparing the obtained results with the ones obtained by some other valid approaches, making use of distance and dispersion measure functions. Computational results show the proposed fuzzy method to be more efficient than other approaches. The encouraging results provide motivations for the use of our proposed fuzzy approach to solving other kinds of multi-objective mixed-integer models.

Nonlinear PID global regulators with selftuned PD gains for robot manipulators: theory and experimentation

It is known that the Proportional Integral and Derivative (PID) regulator with constant and symmetric positive definite gain matrices (Kp, Kv and Ki), in closed loop with robotic manipulators, guarantees semiglobal asymptotic stability of the equilibrium point. In this paper, a fresh approach for control of robots manipulators based on a class of PID regulators with variable gain matrices Kp and Kv, which guarantee global asymptotic stability, is proposed. This PID control scheme, proposed with PD variable gains and fixed integral gains, improves the performance of the transient response and the handling of constraints, such as the limits of the actuator torques. Based on the authors experience, our proposal is the first scheme of a nonlinear PID regulator for robot manipulators whose PD gains are variable and its stability proof is global asymptotic in the sense of Lyapunov. The PD variable gains are directly dependent on the joint position errors and are fine tuned by fuzzy logic methodology. Good results were obtained on an experimental direct-drive robot arm of two degrees of freedom, when it was tested in order to verify the effectiveness of the new controller.

Soil quality assessment using erosion-sensitive indices and fuzzy membership under different cropping systems on a Ferralsol in Brazil

The objective of this study was to evaluate soil quality under different cropping systems through two erosion-sensitive indexing methodologies applying fuzzy membership functions to a minimum data set (MDS) of soil properties. The experiment was conducted in a Ferralsol, in annual growing cycles from 2007 to 2014. The evaluated cropping systems included individual and intercropped treatments using: sunn hemp (Crotalaria juncea L.), pearl millet (Pennisetum glaucum L.), jack bean (Canavalia ensiformis L.), pigeon pea (Cajanus cajan L. Huth), and maize (Zea mays L.). Sampling in an adjacent area under native forest was also performed as a reference ecosystem. Soil properties such as bulk density, micro-, macro- and total porosity, aggregates stability, laboratorial soil fertility properties, and soil organic matter content were selected as MDS of physical and chemical properties, being used to compute two soil quality indices: 1) Integrated Quality Index (IQI), and 2) Nemoro Quality Index (NQI). Soil quality results showed the lowest values of soil and water losses corresponded to the largest soil quality indices evidencing that the usage of water-erosion-sensitive indices can be useful for the prediction of soil quality status. The selected MDS of soil properties were adequate indicators of soil quality as reduced water erosion was associated with large soil quality indices, e.g. negative correlation between soil macroporosity and soil erosion processes. The fuzzy methodology was effective in predicting soil quality using a MDS of soil property indicators, which can benefit various stakeholders in their decision-making process to ensure continuing and sustainable crop production.

A spherical fuzzy methodology integrating maximizing deviation and TOPSIS methods

Due to the uncertainty and vagueness, ambiguity and subjectivity of the information in an intricate decision-making environment, the assessment data specified by experts are mostly fuzzy and uncertain. As an extension of Pythagorean fuzzy sets (PyFSs) and picture fuzzy sets (PFSs), spherical fuzzy sets (SFSs) are used frequently for presenting fuzzy and indeterminate information. In multi-criteria decision-making (MCDM) problems, the weights of criteria are not known generally. The maximizing deviation technique is a useful tool to handle such problems that we have partially or incomplete information about the criteria’ weights. This research expands the classical maximizing deviation technique to the spherical fuzzy maximizing deviation technique using single-valued (SV) and interval-valued (IV) spherical fuzzy sets to determine criteria weights. To rank the alternatives and specify the preeminent preference, we proposed the Interval Valued Spherical Fuzzy TOPSIS method based on the similarity measure instead of distance measure. For this purpose, we proposed an IVSF cosine similarity measure. To present its effectiveness and practicability, we apply the proposed methodology to an advertisement strategy selection problem, where IVSF sets are used to represent the evaluations about alternatives and criteria. A sensitivity analysis with different similarity measurements is performed to show the reliability of the proposed methodology.

Neuro-Fuzzy-Based Frame Pre-Emption Using Time-Sensitive Networking for Industrial Ethernet

IEEE802.1 Time-Sensitive Networking (TSN) makes it conceivable to convey the data traffic of time as well as critical applications using Ethernet shared by different applications having diversified Quality of Service (QoS) requirements for both TSN and non-TSN. TSN assures a guaranteed data delivery with limited latency, low jitter, and amazingly low loss of data for time-critical traffic. By holding networking resources for basic traffic, and applying different queuing and traffic shaping strategies, TSN accomplishes zero congestion loss for basic time-critical traffic. In proposed system, backpropagation algorithm is used to train the training set and fuzzy inference system methodologies such as Mamdani fuzzy inference system which has fuzzy inputs and fuzzy outputs, Sugeno FIS which has fuzzy inputs and a crisp output and adaptive-network-based fuzzy inference system has obtained from the neural network and fuzzy logic. The proposed system uses neuro-fuzzy techniques to handle frame pre-emption and reduces the time taken for decision making. It presents a decision making process using the traffic class.

Relating Hydro-Meteorological Variables to Water Table in an Unconfined Aquifer via Fuzzy Linear Regression

This study aims to assess the short-term response of groundwater to the main hydro-meteorological variables of drought in a coastal unconfined aquifer. For this purpose, a multiple fuzzy linear regression-based methodology is implemented in order to relate rainfall, streamflow and the potential evapotranspiration to groundwater. Fuzzy regression analysis is recommended when there is a lack of data. The uncertainty of the system is incorporated into the regression coefficients which, in this study, are considered to be fuzzy symmetrical triangular numbers. Two objective functions are used producing a fuzzy band in which all the observed data must be included. The first objective function, based on Tanaka’s model, minimizes the total width of the produced fuzzy band. The second one includes the first while additionally minimizing the distance between the central value of the fuzzy output of the model and the observed value. Validity of the model is checked through suitability measures. The present methodology is applied at the east part of the Nestos River Delta in the Prefecture of Xanthi (Greece), where the observed values of the depth of groundwater level of four wells are examined.

Robust topology optimization methodology for continuum structures under probabilistic and fuzzy uncertainties

AbstractOwing to the variations in geometric dimensions, material properties and external loads in engineering applications, robust topology optimization (RTO) has garnered increasing attention in recent years to account for the uncertain behaviors during the preliminary concept design phases. This paper presents a hybrid RTO method to simultaneously resolve the epistemic and aleatory uncertainties. First, based on the probabilistic and fuzzy methodologies, the hybrid RTO model is formulated with nested double optimization loops using Monte Carlo simulations. Second, an efficient iterative method is proposed based on the perturbation method to accelerate the rate of convergence of the proposed hybrid RTO model. The derivatives of the hybrid robust compliance objective function with respect to the deterministic design variables, random parameters, and fuzzy parameters are then derived using the adjoint variable method. Finally, a T‐shaped beam design, an L‐shaped beam design, and a three‐dimensional cantilever beam design are tested to validate the proposed hybrid RTO method.

Smart scheduling of transmission line switching: optimization of multi-objective microgrid’s day-ahead energy scheduling with considering high penetration of green energies and INVELOX

In recent years, distributed generation resources, especially renewable energies, have utilized in different situations; to solve the environmental pollution problems and lack of sufficient energies. In this regard, microgrid’s bi-objective energy scheduling and management are considered with INVELOX wind turbine, micro-turbine, boiler, CHP, photovoltaic, wind turbines, and also energy storage systems to enhance the reliability of the network. Moreover, portable resources have been used to manage the demand side. The remarkable innovation of this paper is to control the transmission line circuit breakers, and as a result, control the ecological contamination and microgrid’s cost; by variation in exchangeable power. In this strategy, the microgrid is capable of changing the tie line capacity and achieve the top-selling or buying electricity to/from the power system by switching the circuit breakers (circuit breakers can switch by operator decision or smart devices if required, at all-time intervals). The proposed strategy is modeled as mixed-integer linear programming. All-out expense and ecological contamination of under-study’s microgrid are considered as the objective function. The EPC technique and fuzzy methodology have been used to tackle the problem and select the ideal arrangement and solution, respectively. The outcomes display the improvement of microgrid operation with the presented energy management strategy, so that cost decreased by about 48.76% and 38.6%, in the last two scenarios, respectively.

Fuzzy Gilmore and Gomory algorithm: Application in robotic flow shops with the effects of job-dependent transportation and set-ups

This paper addresses the scheduling of robotic cells with job-dependent transportation and set-up effects with fuzzy methodology. Since transportation and set-up times are a large portion of the production time in a flexible manufacturing cell, ignoring these parameters may cause significant errors in determining the optimal makespan. Furthermore, determining the exact values of these time parameters is a challenging task. To overcome this problem, we represent these parameters using fuzzy L-R numbers. Using the capability of fuzzy numbers to represent approximate values, we can represent these parameters without losing valuable information. For generating the optimal part sequencing in the cells, the Gilmore and Gomory algorithm is modified, and instead, a fuzzy Gilmore and Gomory algorithm is developed. We compare the results of the proposed fuzzy method with those of crisp ones. The results indicate the superiority of the proposed algorithm in terms of robustness, flexibility, and reduction of makespan.

Fuzzy Methodology for Quantifying Operational Risk Exposure of Financial Institutions

Fuzzy Methodology for Quantifying Operational Risk Exposure of Financial Institutions

A Novel Fuzzy Frequent Itemsets Mining Approach for the Detection of Breast Cancer

Breast cancer, a type of malignant tumor, affects women more than men. About one-third of women with breast cancer die of this disease. Hence, it is imperative to find a tool for the proper identification and early treatment of breast cancer. Unlike the conventional data mining algorithms, fuzzy logic-based approaches help in the mining of association rules from quantitative transactions. In this study, a novel fuzzy methodology, IFFP (improved fuzzy frequent pattern mining), based on a fuzzy association rule mining for biological knowledge extraction, is introduced to analyze the dataset in order to find the core factors that cause breast cancer. It is determined that the factor, mitoses, has low range of values on both malignant and benign, and hence it does not contribute to the detection of breast cancer. On the other hand, the high range of bare nuclei shows more chances for the presence of breast cancer. Experimental evaluations on real datasets show that the proposed method outperforms recently proposed state-of-the-art algorithms in terms of runtime and memory usage.

Measuring financial well-being in Europe using a fuzzy set approach

PurposeThis paper aims to apply a multidimensional approach to assessing the financial well-being of European countries.Design/methodology/approachFinancial well-being is a very complex phenomenon to measure because it is composed of different dimensions. Therefore, this paper uses a multidimensional and fuzzy methodology to assess financial well-being in Europe. The financial well-being fuzzy indicator was calculated using European Quality of Life Survey data.FindingsFinancial well-being is heterogeneous across European countries. This evidence is confirmed both at the level of overall financial well-being and at the level of sub-indices. The degree of financial well-being is not directly related to wealth as traditionally measured (i.e. GDP), but shows some correspondence with socio-economic characteristics of the population and with governance and cultural elements of a country.Practical implicationsUnderstanding financial well-being could help financial institutions to transition from a one-size-fits-all approach to a more tailored approach when they provide financial services and could help policy makers to consider financial well-being when they decide how and where to allocate public spending.Originality/valueTo the best of authors’ knowledge, this study is the first to employ a fuzzy methodology for the analysis of financial well-being in Europe.

Fuzzy finite element analysis for free vibration response of functionally graded semi-rigid frame structures

This study contributes a practical approach for the fuzzy free vibration quantification of functionally graded semi-rigid frame structures. A new Timoshenko beam element is formulated to include the connection rigidity for the analysis purpose. The finite element formulation is general to present different semi-rigid conditions, whereas hinged and rigid connections are special cases. Furthermore, an efficient response-surface-based fuzzy analysis is established based on the α–cut strategy and first-order Taylor's approximation to predict the fuzzy natural frequencies of the structures. Highlighted point is that various input uncertainties, such as the material characteristics, the member dimensions, and the connection rigidities, can be incorporated in the analysis by the presented fuzzy methodology. Computational efficiency and correctness of the proposed method are shown, and the effect of the uncertainties, especially of the connection rigidities, on the natural frequency of semi-rigid FGM structures is explored via solving some numerical examples.

Assessing Food Supply Chain Sustainability Based on the Framework of Fussy TOPSIS

Environmental degradation has been a great concern for Chinese people.  Sustainable management serves a critical avenue to solve environmental problems and has become increasingly popular and important in theoretical and practical terms. Sustainability assessment is conducive to the shift and improvement of sustainability performance. The aim of this article is to propose an evaluation framework through constructing a comparatively comprehensive set of indicators so as to measure one of the most significant yet complex supply chains. The assessment methodology is the Fuzzy TOPSIS methodology which is utilized to ascertain the weights of indicators as well as comparison of different food supply chains. The study enables stakeholders to gain a better understanding of food supply chain sustainability as well as further inform their decisions. Scholars and practitioners in the sphere of sustainable research in China could utilize the findings of the article to take corresponding countermeasures to enhance sustainability of food supply chain so as to relieve the increasingly severe environmental problems.

Optimization of Process Parameters during Pressure Die Casting of A380: a Silicon-Based Aluminium Alloy Using GA & Fuzzy Methodology

Pressure die-casting is generally useful in casting of zinc, Aluminium, magnesium, lead and tin-based alloys. Better surface finish, tolerances and dimensional accuracy can be achieved by optimization of significant process parameters of Pressure die casting (PDC) such as solidifying-time, melting-temperature, fill-up-time, inoculation-pressure and plunger-velocity etc. In addition, with the proper selection of optimum process parameters the PDC defects such as flash, cold-shut, porosity, misrun, etc. also minimized. In the present work, minimization of the PDC defects using artificial intelligent technique comprises of Genetic-algorithm (GA) and fuzzy-logic-methodology (FLM). A380: A silicon based aluminium alloy carburetor-housing is considered for the same. L18 orthogonal array based Taguchi design approach is considered for number of % defects per batch in a lot of 500 components as FLM knowledge base. To estimate the extent of accuracy of predictions made by FLM, regression analysis isperformed. FLM is also validated with R-square approach. Besides this, ANOVA has also been applied to estimate the significance level in terms of percent contribution among the input parameters and defects during PDC. Solidifying-time & fill-up-time has been found most sensitive parameter. Melting-temperature, plunger-velocity and inoculation-pressure are also identified as influencing parameters with comparatively low percent contribution. To optimize process parameters GA is applied and the fitness value is calculated using FLM. From this combination of artificial intelligent approach an optimum combination of process parameter has been obtained. Further, the results are compared with the Taguchi methodology and the approach provides appreciable amount of decrease in PDC-defects by fine tuning the input parameter.