Fuzzy Point Research Articles

A novel method for estimating the failure possibility by combining the adaptive Kriging model with the Markov chain simulation

Under the condition of fuzzy inputs, the structure safety level is generally measured by the failure possibility. Differing from the level cut optimization method and the fuzzy simulation method, a novel method for estimating the failure possibility is proposed in this paper by combining the adaptive Kriging surrogate model with the Markov chain simulation. Based on the principle of fuzzy simulation, the fuzzy design point is defined as the failure point with the maximal joint membership function in the fuzzy simulation sample pool, and the problem of estimating the failure possibility can be transformed into that of searching the fuzzy design point. Then the Markov chain simulation which can quickly simulate the samples in the interest region is employed to search the fuzzy design point by a designed transfer criteria. To efficiently recognize the candidate states of the Markov chain, the Kriging surrogate model is adaptively trained in the candidate sample pool generated by the Markov chain states. By replacing the actual performance function with the convergent Kriging model, the efficiency of Markov chain simulation can be improved greatly. The presented examples are used to prove the rationality and high efficiency of the proposed method.

New Results on I 2 -Statistically Limit Points and I 2 -Statistically Cluster Points of Sequences of Fuzzy Numbers

In this paper, some existing theories on convergence of fuzzy number sequences are extended to I 2 -statistical convergence of fuzzy number sequence. Also, we broaden the notions of I -statistical limit points and I -statistical cluster points of a sequence of fuzzy numbers to I 2 -statistical limit points and I 2 -statistical cluster points of a double sequence of fuzzy numbers. Also, the researchers focus on important fundamental features of the set of all I 2 -statistical cluster points and the set of all I 2 -statistical limit points of a double sequence of fuzzy numbers and examine the relationship between them.

Research on Interval-Valued Intuitionistic Fuzzy Multi-Attribute Decision Making Based on Projection Model

Background: Regarding the multi-attribute decision making where the decision information is the interval-valued intuitionistic fuzzy number and the attribute weight information is not completely determined. Methods: Intuitionistic fuzzy set theory introduces non-membership function, as an extension of the fuzzy set theory, it has certain advantages in solving complex decision-making problems. A projection model based interval-valued intuitionistic fuzzy multi-attribute decision-making scheme was proposed in this study. The objective weight of the attribute was obtained using improved intervalvalued intuitionistic fuzzy entropy, and thus the comprehensive weight of the attribute was obtained according to the preference information. Results: In the aspect of the decision-making matrix processing, the concept of interval-valued intuitionistic fuzzy ideal point and its related concepts were defined, the score vector of each scheme was calculated, the projection model was constructed to measure the similarity between each scheme and the interval-valued intuitionistic fuzzy ideal point, and the scheme was sorted according to the projection value. Conclusion: The efficiency and usability of the proposed approach are considered in the case study.

On 2-absorbing bipolar fuzzy ideals over LA -semigroups

The aim of this manuscript is to apply bipolar fuzzy sets for dealing with several kinds of theories in LA -semigroups. To begin with, we introduce the concept of 2-absorbing (quasi-2-absorbing) bipolar fuzzy ideals and strongly 2-absorbing (quasi-strongly 2-absorbing) bipolar fuzzy ideals in LA -semigroups, and investigate several related properties. In particular, we show that a bipolar fuzzy set A = ( μ A P , μ A N ) over an LA -semigroup S is weakly 2-absorbing if and only if [ B ⊙ C ] ⊙ D ⪯ A implies B ⊙ C ⪯ A or C ⊙ D ⪯ A or B ⊙ D ⪯ A for any bipolar fuzzy sets B = ( μ B P , μ B N ) , C = ( μ C P , μ C N ) and D = ( μ D P , μ D N ) . Also, we give some characterizations of quasi-strongly 2-absorbing bipolar fuzzy ideals over an LA -semigroup S by bipolar fuzzy points. In conclusion of this paper we prove that the relationship between quasi-strongly 2-absorbing bipolar fuzzy ideals over an LA -semigroup S and quasi-2-absorbing bipolar fuzzy ideals over S.

Fuzzy Annihilator Ideals of C -Algebra

In this paper, we introduce the concept of relative fuzzy annihilator ideals in C-algebras and investigate some its properties. We characterize relative fuzzy annihilators in terms of fuzzy points. It is proved that the class of fuzzy ideals of C-algebras forms Heything algebra. We observe that the class of all fuzzy annihilator ideals can be made as a complete Boolean algebra. Moreover, we study the concept of fuzzy annihilator preserving homomorphism. We provide a sufficient condition for a homomorphism to be a fuzzy annihilator preserving.

Remote Sensing Image Segmentation of Pipeline High Consequence Area Based on Bee Colony Strategy Fuzzy MRF Algorithm

Oil pipeline is a kind of high-risk continuous transportation system. High consequence area refers to the area where public life as well as property are endangered and even the environment is polluted after pipeline leakage. Through the analysis of remote sensing images, the position of oil pipeline and the change of its surrounding environment can be determined, and the monitoring and protection of oil pipeline in high consequence area can be realized. Aiming at the problems of low segmentation accuracy, difficulty in obtaining global optimal solution and low efficiency caused by prior knowledge of classical Markov image segmentation. A fuzzy Markov random field algorithm based on artificial bee colony strategy is proposed. Firstly, according to the initial image segmentation results, pixels are divided into definite points and fuzzy points, and only fuzzy points are calculated. Secondly, a Markov algorithm based on artificial bee colony strategy is designed, which can adaptively select potential function parameters for different images. Finally, the improved algorithm is applied to remote sensing image segmentation in high consequence area of oil pipeline. By comparing multiple images, performance parameters and algorithms, it is proved that the improved algorithm has better optimization ability and convergence performance.

Modelling, Design and Control of a Standalone Hybrid PV-Wind Micro-Grid System

The problem of electrical power delivery is a common problem, especially in remote areas where electrical networks are difficult to reach. One of the ways that is used to overcome this problem is the use of networks separated from the electrical system through which it is possible to supply electrical energy to remote areas. These networks are called standalone microgrid systems. In this paper, a standalone micro-grid system consisting of a Photovoltaic (PV) and Wind Energy Conversion System (WECS) based Permanent Magnet Synchronous Generator (PMSG) is being designed and controlled. Fuzzy logic-based Maximum Power Point Tracking (MPPT) is being applied to a boost converter to control and extract the maximum power available for the PV system. The control system is designed to deliver the required energy to a specific load, in all scenarios. The excess energy generated by the PV panel is used to charge the batteries when the energy generated by the PV panel exceeds the energy required by the load. When the electricity generated by the PV panels is insufficient to meet the load’s demands, the extra power is extracted from the charged batteries. In addition, the controller protects the battery banks in all conditions, including normal, overcharging, and overdischarging conditions. The controller should handle each case correctly. Under normal operation conditions (20% < State of Charge (SOC) < 80%), the controller functions as expected, regardless of the battery’s state of charge. When the SOC reaches 80%, a specific command is delivered, which shuts off the PV panel and the wind turbine. The PV panel and wind turbine cannot be connected until the SOC falls below a safe margin value of 75% in this controller. When the SOC goes below 20%, other commands are sent out to turn off the inverter and disconnect the loads. The electricity to the inverter is turned off until the batteries are charged again to a suitable value.

A new intelligent fault diagnosis and prognosis method for wind turbine doubly-fed induction generator

Condition Monitoring and fault-prognosis approaches are typical methods to reduce the energy production cost and Wind Turbine downtime. In this paper, a new CM combinatory system and fault prognosis are proposed based on an adaptive threshold, feature-level fusion, and new degradation indicator and the CM operation is based on a new index Symptom of Degeneration crossing of an adaptive threshold. Also, a new adaptive threshold is proposed based on the fuzzy rules and WT operation point. Fault prognosis is conducted with the Least-Squares Support-Vector Machine method, and Particle Swarm Optimization is employed for the optimum selecting of the wavelet Kernel function and the SVM parameters. The proposed technique is compared with other methods and the simulation results illustrate the PSO-LS-SVM superiorities. The effectiveness of the proposed prognostic structure is evaluated using a WT test-rig prototype. The experimental results demonstrate that the Condition-Based Maintenance is improved by the proposed structure and the RUL is predicted before serious damage occurrences.

A note on (∈, ∈∨q)-fuzzy prime ideals of near-rings

Using the idea of quasi-coincidence of a fuzzy point with a fuzzy set, we discuss a new kind of (∈, ∈∨q)-fuzzy prime (semiprime) ideals of near-rings. The concept of (∈, ∈∨q 0 δ )-fuzzy prime (semiprime) ideals of near-rings are introduced and some of its related properties are investigated.

Application of magnetic liquid slurries and fuzzy grey analysis in polishing nickel-phosphorus coated SKD11 steel

This paper studies the influencing factors on the magnetic material (nickel-phosphorus coated SKD11 steel) polishing process were determined by investigating the distribution of magnetic iron (MIGs) and abrasive grains (AGs) in the working surface of magnetic liquid slurry (MLS). The influence of the factors, namely the diameters of the MIGs and the AGs and working distance (K), on the polishing surface quality was assessed by fuzzy gray analysis. Results showed that the values of the fuzzy gray relationships with the factors were 0.6361, 0.5181, 0.8804, and 0.6464. Result showed that the fuzzy gray relationship point of factors known as K exerted the most remarkable influence on the polishing effect, whereas the effect of MIG diameter was insignificant. The experimental results indicated that MLS containing MIGs with a diameter of 5 μm, AGs with a diameter of 1.5 μm and K = 1 mm should be prepared to create a mirror surface with nickel–phosphorus coated SKD11 steel. An extremely smooth mirror surface with Ra = 0.926 nm is produced under these experimental conditions, leaving no scratches and impurities on the machined surface. The proposed polishing method that uses MLS with MIGs and AGs (which are low-cost and commercially available) is suitable and effective for polishing the surface of magnetic materials with extremely high accuracy.

Fuzzy Corner Metric Based Brain Tumor Segmentation and Grade Classification

The main idea of this work is to reduce the physician time by assessing with computer aided tumor detection Human investigation is the routine technique for brain MRI tumor detection and tumors classification. Interpretation of images is based on organized and explicit classification of brain MRI and also various techniques have been proposed. The proposed system segments the Brain MR images with the help of fuzzy corner points and the segmented tumor area is subjected for grade classification. Support vector machine classifier is implemented for the grade classification. The proposed system is addressing a simple and robust technique for the tumor diagnosis and also the performance is 89.5% which is comparatively high when compared to the traditional classification method.

Preorders and intuitionistic fuzzy topological spaces

In this paper, we associate each intuitionistic fuzzy topology on a set X with two preorders on the set of all intutionistic fuzzy points P t (I X ) on X. Also we introduce the concept of two types of preorders on P t (I X ) namely C-preorders and K-preorders, and shows how these preorders related with the intuitionistic fuzzy topological properties on X.

Notes on Bipolar Interval Valued Intuitionistic Fuzzy Topological Space

In this paper we have introduced Bipolar Interval Valued Intuitionistic Fuzzy Point, Bipolar Interval Valued Intuitionistic Fuzzy Neighbourhood,Bipolar Interval Valued Intuitionistic Fuzzy Interior and Bipolar Interval Valued Intuitionistic Fuzzy Closurein Bipolar Interval Valued Intuitionistic Fuzzy topological space and have verifiedsome of its properties.

Fuzzy intersection graph: a geometrical approach

The aim of fuzzy intersection graph is twofold: to work with objects with continuum grades of membership and to acknowledge their inter-relationships. Its working elements are fuzzy logic and reasoning with the objective of providing accessible representation of objects and relationships having a blurry backdrop. This article embarked a comprehensive study of fuzzy intersection graph using fuzzy geometry by defining fuzzy graph and fuzzy intersection region with the help of fuzzy points and fuzzy line segments. The main motive behind revamping fuzzy graphs using fuzzy geometry is to represent the linguistic variables and their inter-dependencies as an exact reflection of human thinking. We have interpreted a detailed study on parallel fuzzy lines and intersecting fuzzy lines and deduced a mathematical relation between the measure of parallelness and degree of intersection of fuzzy lines. Fuzzy intersection region, depending on the types of intersecting fuzzy sets, plays an important role. We have defined few of the most important and useful classes of fuzzy intersection graph from fuzzy geometric view point that will be helpful in expressing human thoughts and communication of information as has been demonstrated through an application on supplier–purchaser relationship in any business. The present study on fuzzy interval graph in detail, ensued in obtainment of results that nullified previously obtained results arising the question—are fuzzy interval graphs always strong intersection graphs?

On the Completion of Fuzzy Normed Linear Spaces in the Sense of Bag and Samanta

In this paper, the completion of fuzzy normed linear space (in the sense of Bag and Samanta) is studied. First, some properties of convergence fuzzy point sequences are discussed. Specially, we give another characterisation of Q-neighbourhood base of for I-topology introduced by Saheli. Then we show that each fuzzy normed linear space has an (up to isomorphism) unique complete fuzzy normed linear space which contains an uniformly dense in every stratum subspace isomorphic to it.

A Dynamic Distance Measure of Picture Fuzzy Sets and Its Application

Picture fuzzy sets, which are the extension of intuitionistic fuzzy sets, can deal with inconsistent information better in practical applications. A distance measure is an important mathematical tool to calculate the difference degree between picture fuzzy sets. Although some distance measures of picture fuzzy sets have been constructed, there are some unreasonable and counterintuitive cases. The main reason is that the existing distance measures do not or seldom consider the refusal degree of picture fuzzy sets. In order to solve these unreasonable and counterintuitive cases, in this paper, we propose a dynamic distance measure of picture fuzzy sets based on a picture fuzzy point operator. Through a numerical comparison and multi-criteria decision-making problems, we show that the proposed distance measure is reasonable and effective.

Fuzzy maximum power point tracking compared to sliding mode technique for photovoltaic systems based on DC-DC boost converter

Aim. This paper presents the amelioration of maximum power point tracking using fuzzy logic methods for photovoltaic system supplying a standalone system. Method. The main role of the maximum power tracking is to force the system for working at the maximum point for each change of meteorological conditions. The classic technique Perturb and Observe is more attractive due to its simple and high efficiency. Sliding mode is a non-linear control technique; characterised by robustness against the parameters change or disturbances, it gives a good maximum power operation under different conditions such as changing solar radiation and photovoltaic cell temperature. Novelty. Fuzzy logic tracking technique is treated. Fuzzy rules construction is based on Perturb and Observe behaviour when the appropriate disturbance step is produced in order to obtain a fast system with an acceptable precision. We use in our study 60 W photovoltaic panel associated to boost chopper converter in order to supply a standalone system. Results. As show in results figures using fuzzy maximum power point tracking the ameliorate performances especially the very low oscillation rate (nearly 0.6 W), and very acceptable response time 0.1 s.

Adaptive Fuzzy PID Based on Granular Function for Proton Exchange Membrane Fuel Cell Oxygen Excess Ratio Control

An effective oxygen excess ratio control strategy for a proton exchange membrane fuel cell (PEMFC) can avoid oxygen starvation and optimize system performance. In this paper, a fuzzy PID control strategy based on granular function (GFPID) was proposed. Meanwhile, a proton exchange membrane fuel cell dynamic model was established on the MATLAB/Simulink platform, including the stack model system and the auxiliary system. In order to avoid oxygen starvation due to the transient variation of load current and optimize the parasitic power of the auxiliary system and the stack voltage, the purpose of optimizing the overall operating condition of the system was finally achieved. Adaptive fuzzy PID (AFPID) control has the technical bottleneck limitation of fuzzy rules explosion. GFPID eliminates fuzzification and defuzzification to solve this phenomenon. The number of fuzzy rules does not affect the precision of GFPID control, which is only related to the fuzzy granular points in the fitted granular response function. The granular function replaces the conventional fuzzy controller to realize the online adjustment of PID parameters. Compared with the conventional PID and AFPID control, the feasibility and superiority of the algorithm based on particle function are verified.

A novel mixed uncertainty support vector machine method for structural reliability analysis

For structures with both random and fuzzy uncertainty parameters, a novel method for obtaining the membership function of reliability in the fuzziness failure criterion is presented using a Random Fuzzy Support Vector Machine based on the Particle Swarm Optimization method (PSO-RFSVM). The proposed method is used to solve the structural reliability problem with implicit limit state function in the presence of mixed variables. Under each cut level, a new distance measure between the mixed variables is presented, named the advanced Yang distance; then, the RFSVM model can be constructed using the kernel function built by the advanced Yang distance and random fuzzy mixed sampling points. Furthermore, to obtain satisfactory fitting, a PSO algorithm is used to optimize the super-parameters in RFSVM. The limit state function is subsequently approximated at the given cut level, and then, the reliability bound under the given cut level is readily obtained. The PSO-RFSVM method provides a new efficient analysis framework in the case of implicit approximation under mixed uncertainty when the classical method has excessive consumption in virtual prototype simulation. Two examples are used to demonstrate the validity and advantage of PSO-RFSVM compared to the Extreme Response Surface based on Simulated Annealing (SAERS) method and Monte Carlo simulation (MCS).

Evaluating the ability of numerical models to capture important shifts in environmental time series: A fuzzy change point approach

Numerical models are essential tools for understanding the complex and dynamic nature of the natural environment. The ability to evaluate how well these models represent reality is critical in their use and future development. This study presents a combination of changepoint analysis and fuzzy logic to assess the ability of numerical models to capture local scale temporal events seen in observations. The fuzzy union based metric factors in uncertainty of the changepoint location to calculate individual similarity scores between the numerical model and reality for each changepoint in the observed record. The application of the method is demonstrated through a case study on a high resolution model dataset which was able to pick up observed changepoints in temperature records over Greenland to varying degrees of success. The case study is presented using the DataLabs framework, a cloud-based collaborative platform which simplifies access to complex statistical methods for environmental science applications.