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Imperialist Competitive Algorithm Optimised Adaptive Neuro Fuzzy Controller for Hybrid Force Position Control of an Industrial Robot Manipulator: A Comparative Study 帝国竞争算法优化自适应神经模糊控制器在工业机械臂混合力位置控制中的比较研究
IF 1.2 Q2 MATHEMATICS, APPLIED Pub Date : 2020-10-01 DOI: 10.1080/16168658.2021.1921378
Himanshu Chaudhary, V. Panwar, N. Sukavanam, Bhawna Chahar
Due to the nonlinear nature of the dynamics of a robot manipulator, controlling the robot meticulously is a challenging issue for control engineers. The key purpose of this paper is to provide an accurate intelligent method for refining the functionality of orthodox PID controller in the problem of force/position control of a robot manipulator with unspecified robot dynamics during external disturbances. A grouping of imperialist competitive algorithm (ICA) and adaptive neuro fuzzy logic is applied for the tuning of PID parameters. This, therefore, forms an intelligent structure, adaptive neuro fuzzy inference system with proportional derivative plus integral (ANFISPD + I) controller, which is more precise in definite and indefinite circumstances. To show the efficiency of the proposed method, this algorithm is applied to solve constrained dynamic force/position control problem of PUMA robot manipulator. The simulated results are compared to those achieved from other evolutionary techniques such as Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). The simulation results exhibit that ICA-based ANFISPD + I outperforms the other evolutionary techniques. Highlights An ICA-ANFISPD + I-based hybrid force/position controller has been proposed. Easy to implement. Works well in the case of disturbances. Actuator Dynamics has been considered. External disturbances have been considered. Robot dynamics are unknown.
由于机器人机械臂动力学的非线性特性,对机器人的精细控制是控制工程师面临的一个具有挑战性的问题。本文的主要目的是提供一种精确的智能方法来完善传统PID控制器在外部干扰下未指定机器人动力学的机器人机械臂力/位置控制问题中的功能。采用一组帝国主义竞争算法(ICA)和自适应神经模糊逻辑对PID参数进行整定。这就形成了一个具有比例导数加积分(ANFISPD + I)控制器的智能结构自适应神经模糊推理系统,在确定和不确定情况下都更加精确。为验证该方法的有效性,将该算法应用于PUMA机器人机械手的约束动态力/位置控制问题。模拟结果与其他进化技术如遗传算法(GA)和粒子群优化(PSO)的结果进行了比较。仿真结果表明,基于ica的ANFISPD + I优于其他进化技术。提出了一种基于ICA-ANFISPD + i的混合力/位置控制器。易于实现。在有干扰的情况下效果很好。执行器动力学已被考虑。考虑了外部干扰。机器人动力学是未知的。
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引用次数: 2
Credit Risk Assessment Using Learning Algorithms for Feature Selection 基于学习算法的特征选择信用风险评估
IF 1.2 Q2 MATHEMATICS, APPLIED Pub Date : 2020-10-01 DOI: 10.1080/16168658.2021.1925021
Z. Hassani, Mohsen Alambardar Meybodi, Vahid Hajihashemi
Firefly algorithm is one of the latest outstanding bio-inspired algorithms, which could be manipulated in solving continuous or discrete optimisation problems. In this context, we have utilised the firefly algorithm accompanied by five well-known models of feature selection classifiers to have an accurate estimation of risk, and further to improve the interpret-ability of credit card prediction. One of the significant challenges in the real-world datasets is how to select features. As most of the datasets are unbalanced, the selection of features turns to the maximum class of data that is not fair. To overcome this issue, we have balanced the data using the SMOTE method. Our experimental results on four datasets show that balancing data has increased accuracy. In addition, using a hybrid firefly algorithm, the optimal combination of features that predicts the target class label is achieved. The selected features by the proposed method besides been reduced can represent both majority and minority classes.
萤火虫算法是最新的杰出的仿生算法之一,它可以用于解决连续或离散优化问题。在此背景下,我们利用萤火虫算法和五种知名的特征选择分类器模型来准确估计风险,并进一步提高信用卡预测的可解释性。在现实世界的数据集中,一个重要的挑战是如何选择特征。由于大多数数据集是不平衡的,特征的选择转向最大类别的数据,这是不公平的。为了克服这个问题,我们使用SMOTE方法平衡了数据。我们在四个数据集上的实验结果表明,平衡数据提高了精度。此外,采用混合萤火虫算法,实现了预测目标类标号的最优特征组合。该方法所选取的特征除了经过约简之外,还可以代表多数类和少数类。
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引用次数: 4
Fuzzy Membership Function Evaluation by Non-Linear Regression: An Algorithmic Approach 非线性回归模糊隶属函数评价的一种算法方法
IF 1.2 Q2 MATHEMATICS, APPLIED Pub Date : 2020-10-01 DOI: 10.1080/16168658.2021.1911567
Rupak Bhattacharyya, S. Mukherjee
ABSTRACT In most researches on fuzzy sets and its application, it is found that the consideration of membership function is predetermined and mostly linear in nature. Extraction and evaluation of non-linear fuzzy membership function that can update itself with in different paradigms is still a matter of great concern to researchers. Here, we discuss 33 different membership function evaluation methodologies published between 1971 and 2016. In a approach to solve the problem, this paper presents a novel algorithm based non-linear fuzzy membership function evaluation scheme with the help of regression analysis and algebra. Three different case studies are done to check the applicability and tractability of the method. A comparative analysis with recent literature justifies the robustness of the proposed method.
在大多数关于模糊集及其应用的研究中,发现隶属函数的考虑是预先确定的,本质上大多是线性的。能够在不同范式下自我更新的非线性模糊隶属函数的提取与评价一直是研究人员关注的问题。在这里,我们讨论了1971年至2016年间发表的33种不同的隶属函数评估方法。为了解决这一问题,本文提出了一种基于回归分析和代数的非线性模糊隶属函数评价方案。通过三个不同的案例研究来验证该方法的适用性和可追溯性。与最近文献的比较分析证明了所提出方法的稳健性。
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引用次数: 2
TOPSIS Method and Similarity Measures Based on Cosine Function Using Picture Hesitant Fuzzy Sets and its Applications to Strategic Decision Making 基于余弦函数的TOPSIS方法和相似性测度及其在战略决策中的应用
IF 1.2 Q2 MATHEMATICS, APPLIED Pub Date : 2020-07-02 DOI: 10.1080/16168658.2020.1866853
T. Mahmood, Zeeshan Ahmad, Zeeshan Ali, K. Ullah
Picture hesitant fuzzy set (PHFS) is a recently developed tool to cope with uncertain and awkward information in realistic decision issues and is applicable where opinions are of more than two types, i.e., yes, no, abstinence and refusal. Similarity measures (SMs) in a data mining context are distance with dimensions representing features of the objects. Keeping the advantages of the above analysis, in this manuscript, the authors proposed SMs for PHFSs, including cosine SMs for PHFSs, SMs for PHFSs based on cosine function, and SMs for PHFSs based on cotangent function. Further, entropy measure, TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based on correlation coefficient are investigated for PHFSs. Further, some weighted SMs are also proposed and then applied to the strategic decision-making problem and the results are discussed. Moreover, we take two illustrative examples to compare the established work with existing drawbacks and also show that the existing drawback cannot solve the problem of established work. But on the other hand, the new approach can easily solve the problem of the existing drawback. Finally, the advantages of the new approach are discussed.
图片犹豫模糊集(PHFS)是最近发展起来的一种工具,用于处理现实决策问题中不确定和尴尬的信息,适用于两种以上类型的意见,即“是”、“否”、“禁欲”和“拒绝”。数据挖掘上下文中的相似性度量(SMs)是与表示对象特征的维度之间的距离。保留上述分析的优点,在本文中,作者提出了phfs的SMs,包括phfs的余弦SMs,基于余弦函数的phfs SMs和基于余切函数的phfs SMs。在此基础上,研究了基于相关系数的熵测度、TOPSIS (technical for Order of Preference by Similarity to Ideal Solution)。在此基础上,提出了一些加权模型,并将其应用于战略决策问题,并对结果进行了讨论。此外,我们还举了两个说明性的例子,将现有的工作与现有的缺陷进行了比较,也说明了现有的缺陷并不能解决现有工作的问题。但另一方面,新方法可以很容易地解决现有缺点的问题。最后,讨论了新方法的优点。
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引用次数: 1
On KM-Fuzzy Metric Hypergraphs 关于km -模糊度量超图
IF 1.2 Q2 MATHEMATICS, APPLIED Pub Date : 2020-07-02 DOI: 10.1080/16168658.2020.1867419
M. Hamidi, Sirus Jahanpanah, A. Radfar
ABSTRACT This paper, applies the concept of KM-fuzzy metric spaces and introduces a novel concept of KM-fuzzy metric hypergraphs based on KM-fuzzy metric spaces. In special cases, we add some conditions to axioms of KM-fuzzy metric hypergraphs(to obtain of elementary hypergraphs, C-accessible hypergraphs, Cor-able hypergraphs, fuzzy hypergraphs) and so obtain locally strong KM-fuzzy metric hypergraphs and strong KM-fuzzy metric hypergraphs. This study, investigates on the finite KM-fuzzy metric spaces with respect to metrics, KM-fuzzy metrics and constructs KM-fuzzy metric spaces on any given non-empty sets. It tries to extend the concept of KM-fuzzy metric spaces to union of KM-fuzzy metric spaces and product of KM-fuzzy metric spaces and in this regard investigates on union and product of KM-fuzzy metric hypergraphs.
本文应用km -模糊度量空间的概念,在km -模糊度量空间的基础上提出了km -模糊度量超图的新概念。在一些特殊情况下,我们在km -模糊度量超图的公理中添加一些条件(得到初等超图、c -可达超图、可达超图、模糊超图),从而得到局部强km -模糊度量超图和强km -模糊度量超图。本文研究了有限km -模糊度量空间与度量、km -模糊度量的关系,并在任意给定的非空集上构造了km -模糊度量空间。试图将km -模糊度量空间的概念推广到km -模糊度量空间的并和与积,并在此基础上研究了km -模糊度量超图的并和与积。
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引用次数: 3
Integrated Possibilistic Linear Programming with Beta-Skewness Degree for a Fuzzy Multi-Objective Aggregate Production Planning Problem Under Uncertain Environments 不确定环境下模糊多目标总体生产计划问题的具有β偏度的综合可能性线性规划
IF 1.2 Q2 MATHEMATICS, APPLIED Pub Date : 2020-07-02 DOI: 10.1080/16168658.2021.1893493
Noppasorn Sutthibutr, N. Chiadamrong
This study proposes an improved Fuzzy Programming (FP) approach to optimise multi-objective Aggregate Production Planning (APP) problem under uncertain environments. The proposed approach integrates the concept of Possibilistic Linear Programming (PLP) with Beta-Skewness Degree that decision-makers can manipulate the best level of data fuzziness as well as maintain such fuzziness in the optimisation process (by not turning it to deterministic data too early). The effectiveness of the proposed approach is demonstrated through a case study by minimising the highest overall deviation from the ideal solution of total costs under imprecise operating costs, customer demand, labour level, and machine capacity. Our comparative result clearly shows that the obtained solution outperforms the solutions from traditional defuzzification methods. The proposed approach also helps decision-makers not only to know and optimise the most likely situation, but also realise the outcomes in the optimistic and the pessimistic business situations so that decision makers can prepare and take necessary actions for future uncertainty.
提出了一种改进的模糊规划方法来求解不确定环境下的多目标综合生产计划问题。所提出的方法将可能性线性规划(PLP)的概念与β -偏度相结合,决策者可以操纵数据模糊的最佳水平,并在优化过程中保持这种模糊性(通过不过早地将其转向确定性数据)。通过一个案例研究,在不精确的运营成本、客户需求、劳动力水平和机器容量的情况下,最小化与理想总成本解决方案的最高总体偏差,证明了所提出方法的有效性。我们的比较结果清楚地表明,所得到的解优于传统的解模糊化方法。所提出的方法还可以帮助决策者不仅了解和优化最可能的情况,而且还可以实现乐观和悲观业务情况下的结果,以便决策者可以为未来的不确定性做好准备并采取必要的行动。
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引用次数: 2
Numerical Accuracy of the Predictor-Corrector Method to Solve Fuzzy Differential Equations Based on the Stochastic Arithmetic 基于随机算法求解模糊微分方程的预测-校正法的数值精度
IF 1.2 Q2 MATHEMATICS, APPLIED Pub Date : 2020-07-02 DOI: 10.1080/16168658.2021.1880134
M. A. Fariborzi Araghi, Hasan Barzegar Kelishami
In this work, a reliable scheme is proposed to solve fuzzy differential equation based on the predictor-corrector methods (PC-methods) under generalized H-differentiability. For this purpose, the stochastic arithmetic(SA) and the CESTAC* method are applied to validate the results. Also, the numerical accuracy of the method is proved and an algorithm is given based on the new arithmetic. In order to implement C++ codes, the CADNA† library is used. In this case, the optimal number of nodes and optimal step size are found. The examples illustrate the efficiency and importance of using the SA in place of the floating-point arithmetic(FPA).
在广义h -可微性条件下,提出了一种基于预测校正方法(pc -方法)求解模糊微分方程的可靠方案。为此,采用随机算法(SA)和CESTAC*方法对结果进行验证。证明了该方法的数值精度,并在此基础上给出了一种算法。为了实现c++代码,使用了CADNA†库。在这种情况下,找到最优节点数和最优步长。这些示例说明了使用SA代替浮点算术(FPA)的效率和重要性。
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引用次数: 2
Extension of Duality Results and a Dual Simplex Method for Linear Programming Problems With Intuitionistic Fuzzy Variables 具有直观模糊变量的线性规划问题对偶结果的推广及对偶单纯形方法
IF 1.2 Q2 MATHEMATICS, APPLIED Pub Date : 2020-07-02 DOI: 10.1080/16168658.2021.1908818
M. Goli, S. H. Nasseri
The aim of this paper is to introduce a formulation of linear programming problems involving intuitionistic fuzzy variables. Here, we will focus on duality and a simplex-based algorithm for these problems. We classify these problems into two main different categories: linear programming with intuitionistic fuzzy numbers problems and linear programming with intuitionistic fuzzy variables problems. The linear programming with intuitionistic fuzzy numbers problem had been solved in the previous literature, based on this fact we offer a procedure for solving the linear programming with intuitionistic fuzzy variables problems. In methods based on the simplex algorithm, it is not easy to obtain a primal basic feasible solution to the minimization linear programming with intuitionistic fuzzy variables problem with equality constraints and nonnegative variables. Therefore, we propose a dual simplex algorithm to solve these problems. Some fundamental concepts and theoretical results such as basic solution, optimality condition and etc., for linear programming with intuitionistic fuzzy variables problems, are established so far. Moreover, the weak and strong duality theorems for linear programming with intuitionistic fuzzy variables problems are proved. In the end, the computational procedure of the suggested approach is shown by numerical examples.
本文的目的是引入一个包含直觉模糊变量的线性规划问题的公式。在这里,我们将专注于对偶性和基于simplex的算法来解决这些问题。我们将这些问题分为两大类:直觉模糊数线性规划问题和直觉模糊变量线性规划问题。以往的文献已经解决了带有直觉模糊数的线性规划问题,在此基础上给出了求解带有直觉模糊变量的线性规划问题的一种方法。在基于单纯形算法的方法中,对于具有等式约束和非负变量的直觉模糊变量最小化线性规划问题,不容易得到一个原始的基本可行解。因此,我们提出一种对偶单纯形算法来解决这些问题。本文建立了直觉模糊变量线性规划问题的基本解、最优性条件等基本概念和理论结果。此外,还证明了具有直觉模糊变量的线性规划问题的弱对偶定理和强对偶定理。最后,通过数值算例说明了该方法的计算过程。
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引用次数: 1
Fuzzy Regression in Predicting Math Achievement, Based on Philosophic-Mindedness, Creativity, Mathematics Self-efficacy, and Mathematics Self-concept 基于哲学思维、创造力、数学自我效能感和数学自我概念的模糊回归预测数学成绩
IF 1.2 Q2 MATHEMATICS, APPLIED Pub Date : 2020-07-02 DOI: 10.1080/16168658.2021.1880142
Seyyed Meisam Taheri, M. Asadi, A. Shiralipour
ABSTRACT The present study proposes a flexible/soft model for investigating the role of philosophic-mindedness, creativity, mathematics self-efficacy, and mathematics self-concept in predicting math achievement. To do this end, a fuzzy regression model is developed. Two common criteria are used to evaluate the obtained model. Moreover, the predictability of the model is explained. The case study involves 28 male students from Marand, Iran (year 2015–2016) who took part in a test of mathematics achievement. The participants were junior high-school students in science field of study who were asked to answer four questionnaires pertaining to philosophic-mindedness, creativity, mathematics self-efficacy, and mathematics self-concept. The analysis of the results through fuzzy regression revealed that philosophical-mindedness is not linked to participants' math achievement, while the variables of creativity, mathematics self-efficacy, and mathematics self-concept are positively correlated. The results of this study provide suggestions to any educational system in planning for students' math achievement growth. The proposed methodology is general, so that it can be employed in other educational levels and fields of study.
摘要:本研究提出了一个柔性/软模型来探讨哲学思维、创造力、数学自我效能感和数学自我概念在预测数学成绩中的作用。为此,建立了模糊回归模型。两个常见的标准被用来评估得到的模型。此外,还解释了模型的可预测性。该案例研究涉及来自伊朗马兰的28名男学生(2015-2016年),他们参加了一项数学成绩测试。研究对象为理工科初中生,他们被要求回答关于哲学思维、创造力、数学自我效能感和数学自我概念的四份问卷。通过模糊回归分析结果发现,哲学思维与被试数学成绩不相关,而创造力、数学自我效能感、数学自我概念等变量与数学成绩呈正相关。本研究的结果为任何教育系统在规划学生数学成绩成长时提供建议。所建议的方法是通用的,因此它可以用于其他教育水平和研究领域。
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引用次数: 1
Stronger Forms of Sensitivity for Induced Fuzzified Map 诱导模糊化映射的更强灵敏度形式
IF 1.2 Q2 MATHEMATICS, APPLIED Pub Date : 2020-07-02 DOI: 10.1080/16168658.2021.1915450
Praveen Kumar, Ayub Khan
Every dynamical system on a compact metric space X induces a fuzzy dynamical system on the space of fuzzy sets , by Zadeh's extension principle. In this paper we consider stronger forms of sensitivity, viz. strong sensitivity, asymptotic sensitivity, syndetic sensitivity, multi-sensitivity and cofinite sensitivity. Some examples are given to expound the interrelation between them. Our main concern here is to find the relationship between f and in terms of these forms of sensitivity. We Prove that these forms of sensitivity for f partially imply the same for and in other way we also get partial induction.
利用Zadeh的可拓原理,在紧度量空间X上的每一个动力系统都可以导出一个模糊集空间上的模糊动力系统。本文考虑了灵敏度的较强形式,即强灵敏度、渐近灵敏度、综合灵敏度、多灵敏度和有限灵敏度。举例说明了它们之间的相互关系。我们主要关心的是找到f和之间的关系,用这些形式的灵敏度表示。我们证明了f的灵敏度的这些形式部分地蕴涵了同样的意义,另外我们也得到了部分归纳法。
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引用次数: 0
期刊
Fuzzy Information and Engineering
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