Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

A Stable Fuzzy-Based Computational Model and Control for Inductions Motors

In this paper,a stable and adaptive sliding mode control(SMC)method for induction motors is introduced.Determining the parameters of this system has been one of the existing challenges.To solve this challenge,a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism.According to the dynamic changes of the system,in addition to the parameters of the SMC,the parameters of the type-2 fuzzy neural network are also updated online.The conditions for guaranteeing the convergence and stability of the control system are provided.In the simulation part,in order to test the proposed method,several uncertain models and load torque have been applied.Also,the results have been compared to the SMC based on the type-1 fuzzy system,the traditional SMC,and the PI controller.The average RMSE in different scenarios,for type-2 fuzzy SMC,is 0.0311,for type-1 fuzzy SMC is 0.0497,for traditional SMC is 0.0778,and finally for PI controller is 0.0997.

Aggregation Operators for Decision Making Based on q-Rung Orthopair Fuzzy Hypersoft Sets:An Application in Real Estate Project

In this paper,a decision-making problem with a q-rung orthopair fuzzy hypersoft environment is developed,and two operators of ordered weighted average and induced ordered weighted average are developed.Several fundamental features are also derived.The induced ordered weighted average operator is essential in a q-ROFH environment as the induced ordered aggregation operators are special cases of the existing aggregation operators that already exist in q-ROFH environments.The main function of these operators is to help decision-makers gain a complete understanding of uncertain facts.The proposed aggregation operator is applied to a decision-making problem,with the aim of selecting the most promising real estate project for investment.

Efficient Numerical Scheme for Solving Large System of Nonlinear Equations

A fifth-order family of an iterative method for solving systems of nonlinear equations and highly nonlinear boundary value problems has been developed in this paper.Convergence analysis demonstrates that the local order of convergence of the numerical method is five.The computer algebra system CAS-Maple,Mathematica,or MATLAB was the primary tool for dealing with difficult problems since it allows for the handling and manipulation of complex mathematical equations and other mathematical objects.Several numerical examples are provided to demonstrate the properties of the proposed rapidly convergent algorithms.A dynamic evaluation of the presented methods is also presented utilizing basins of attraction to analyze their convergence behavior.Aside from visualizing iterative processes,this methodology provides useful information on iterations,such as the number of diverging-converging points and the average number of iterations as a function of initial points.Solving numerous highly nonlinear boundary value problems and large nonlinear systems of equations of higher dimensions demonstrate the performance,efficiency,precision,and applicability of a newly presented technique.

A Non-Singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.

Multimodal Fuzzy Downstream Petroleum Supply Chain:A Novel Pentagonal Fuzzy Optimization

The petroleum industry has a complex,inflexible and challenging supply chain(SC)that impacts both the national economy as well as people’s daily lives with a range of services,including transportation,heating,electricity,lubricants,as well as chemicals and petrochemicals.In the petroleum industry,supply chain management presents several challenges,especially in the logistics sector,that are not found in other industries.In addition,logistical challenges contribute significantly to the cost of oil.Uncertainty regarding customer demand and supply significantly affects SC networks.Hence,SC flexibility can be maintained by addressing uncertainty.On the other hand,in the real world,decision-making challenges are often ambiguous or vague.In some cases,measurements are incorrect owing to measurement errors,instrument faults,etc.,which lead to a pentagonal fuzzy number(PFN)which is the extension of a fuzzy number.Therefore,it is necessary to develop quantitative models to optimize logistics operations and supply chain networks.This study proposed a linear programming model under an uncertain environment.The model minimizes the cost along the refineries,depots,multimode transport and demand nodes.Further developed pentagonal fuzzy optimization,an alternative approach is developed to solve the downstream supply chain using themixed-integer linear programming(MILP)model to obtain a feasible solution to the fuzzy transportation cost problem.In this model,the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number.Furthermore,defuzzification is performed using an accuracy function.To validate the model and technique and feasibility solution,an illustrative example of the oil and gas SC is considered,providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.

Stable Computer Method for Solving Initial Value Problems with Engineering Applications

Engineering and applied mathematics disciplines that involve differential equations in general,and initial value problems in particular,include classical mechanics,thermodynamics,electromagnetism,and the general theory of relativity.A reliable,stable,efficient,and consistent numerical scheme is frequently required for modelling and simulation of a wide range of real-world problems using differential equations.In this study,the tangent slope is assumed to be the contra-harmonic mean,in which the arithmetic mean is used as a correction instead of Euler’s method to improve the efficiency of the improved Euler’s technique for solving ordinary differential equations with initial conditions.The stability,consistency,and efficiency of the system were evaluated,and the conclusions were supported by the presentation of numerical test applications in engineering.According to the stability analysis,the proposed method has a wider stability region than other well-known methods that are currently used in the literature for solving initial-value problems.To validate the rate convergence of the numerical technique,a few initial value problems of both scalar and vector valued types were examined.The proposed method,modified Euler explicit method,and other methods known in the literature have all been used to calculate the absolute maximum error,absolute error at the last grid point of the integration interval under consideration,and computational time in seconds to test the performance.The Lorentz system was used as an example to illustrate the validity of the solution provided by the newly developed method.The method is determined to be more reliable than the commonly existing methods with the same order of convergence,as mentioned in the literature for numerical calculations and visualization of the results produced by all the methods discussed,Mat Lab-R2011b has been used.

Bipolar Interval-Valued Neutrosophic Optimization Model of Integrated Healthcare System

Bipolar Interval-valued neutrosophic set is another generalization of fuzzy set,neutrosophic set,bipolar fuzzy set and bipolar neutrosophic set and thus when applied to the optimization problem handles uncertain data more efficiently and flexibly.Current work is an effort to design a flexible optimization model in the backdrop of interval-valued bipolar neutrosophic sets.Bipolar interval-valued neutrosophic membership grades are picked so that they indicate the restriction of the plausible infringement of the inequalities given in the problem.To prove the adequacy and effectiveness of the method a unified system of sustainable medical healthcare supply chain model with an uncertain figure of product complaints is used.Time,quality and cost are considered as satisfaction level to choose best supplier for medicine procurement.The proposed model ensures 99%satisfaction for cost reduction,63%satisfaction for the quality of product and 64%satisfaction for total time taken in medicine supply chain.

Computer Oriented Numerical Scheme for Solving Engineering Problems

In this study,we construct a family of single root finding method of optimal order four and then generalize this family for estimating of all roots of non-linear equation simultaneously.Convergence analysis proves that the local order of convergence is four in case of single root finding iterative method and six for simultaneous determination of all roots of non-linear equation.Some non-linear equations are taken from physics,chemistry and engineering to present the performance and efficiency of the newly constructed method.Some real world applications are taken from fluid mechanics,i.e.,fluid permeability in biogels and biomedical engineering which includes blood Rheology-Model which as an intermediate result give some nonlinear equations.These non-linear equations are then solved using newly developed simultaneous iterative schemes.Newly developed simultaneous iterative schemes reach to exact values on initial guessed values within given tolerance,using very less number of function evaluations in each step.Local convergence order of single root finding method is computed using CAS-Maple.Local computational order of convergence,CPU-time,absolute residuals errors are calculated to elaborate the efficiency,robustness and authentication of the iterative simultaneous method in its domain.