Fuzzy Membership Function Evaluation by Non-Linear Regression: An Algorithmic ApproachBhattacharyya, Rupak; Mukherjee, Supratim
2020 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1911567
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.
Imperialist Competitive Algorithm Optimised Adaptive Neuro Fuzzy Controller for Hybrid Force Position Control of an Industrial Robot Manipulator: A Comparative StudyChaudhary, Himanshu; Panwar, Vikas; Sukavanam, N.; Chahar, Bhawna
2020 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1921378
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.
Reliability Range Through Upgraded Operation with Trapezoidal Fuzzy NumberKumar, Amit; Dhiman, Pooja
2020 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1918039
This work presents an application of upgraded arithmetic operations on trapezoidal fuzzy number to extend the reliability from point estimation to interval estimation. A realistic example of an accidental case has been taken to show the desired results. As we know that uncertainty is a disregardable phenomenon and in this situation crisp reliability may not be exact. To get a better hold on this situation, we introduce a method of trapezoidal fuzzy number and upgraded arithmetic operations. In this paper, the reliability of each factor is represented by the trapezoidal fuzzy number and the final reliability is obtained in the form of trapezoidal fuzzy number. Hence, the dejection of fuzzy number is preserved and the crisp region is increased to illustrate the highest membership grade as an interval. So it can help the decision maker to analyse the behaviour of the system and take effective decision accordingly to reduce the chance of mishappening.
Solving Two Coupled Fuzzy Sylvester Matrix Equations Using Iterative Least-squares SolutionsBayoumi, Ahmed M. E.; Ramadan, Mohamed A.
2020 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1923442
In this paper, five iterative methods for solving two coupled fuzzy Sylvester matrix equations are considered. The two coupled fuzzy Sylvester matrix equations are expressed by using the generalized inverse of the coefficient matrix, then iterative solutions are constructed by applying the hierarchical identification principle and by using the block-matrix inner product (the star product for short). A proposed modification to this algorithm to solve the first coupled fuzzy Sylvester matrix equations is suggested. This proposed modification is compared with the first algorithm where our modification exhibits fast convergence behavior. Also, we suggested two least-squares iterative algorithm by applying a hierarchical identification principle to solve the two coupled fuzzy Sylvester matrix equations. The proposed methods are illustrated by numerical examples.
Characterization of Non-Associative Ordered Semigroups by the Properties of F-IdealsKausar, Nasreen; Munir, M.; Kousar, Sajida; Gulistan, M.; Anitha, K.
2020 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1924513
In this article, we present brief description on R-OAG-gpd regular ordered AG-groupoid and IR-OAG-gpd intra-regular ordered AG-groupoid by using FL-ideal, FR-ideal, FQ-ideal, FB-ideal, FB-ideal, FGB-ideal.
Weak Forms of Soft Separation Axioms and Fixed Soft PointsAl-shami, T. M.; Abo-Tabl, E. A.; Asaad, B. A.
2020 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1924528
Realizing the importance of separation axioms in classifications of topological spaces and studying certain properties of fixed points, we formulate new soft separation axioms, namely tt-soft and tt-soft b-regular spaces. Their definitions depend on three factors: soft b-open sets, total belong and total non-belong relations. In fact, they are genuine generalizations of p-soft -spaces in the cases of i = 0, 1, 2. With the help of examples, we study the relationships between them as well as with soft and soft b-regular spaces. Some interesting properties of them are obtained under the conditions of soft hyperconnected and extended soft topological spaces. Also, we show that they are preserved under finite product soft spaces and soft -homeomorphism mappings. Finally, we introduce a concept of b-fixed soft points and investigate its main properties.
Credit Risk Assessment Using Learning Algorithms for Feature SelectionHassani, Zeinab; Alambardar Meybodi, Mohsen; Hajihashemi, Vahid
2020 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1925021
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.