Type-2 Fuzzy Method Research Articles

Data Driven Type-2 Fuzzy Modeling and Control of Personalized Thermal Comfort

Since the room thermal comfort has high uncertainties, it is difficult but necessary to study the modeling and control of thermal comfort. This paper tries to solve the modeling and control problem for the room thermal comfort by utilizing the temperature and humidity data collected from the working or living room. Firstly, in order to discard the noisy data and to obtain the reasonable intervals for modeling the temperature and humidity, a statistic method based preprocessing strategy is presented. Secondly, the Gaussian interval type-2 fuzzy set models are constructed to depict the personalized temperature and humidity comfort by measuring the uncertainty degrees of the obtained intervals. Thirdly, a interval type-2 fuzzy method based control scheme is proposed to realize the personalized thermal comfort regulation. Finally, some experimental results are given. And, the results show that the proposed thermal comfort models and control scheme can not only recommend a reasonable temperature and humidity range, but also can realize the optimal thermal comfort control.

The effect of an event-triggered controller on non-strict feedback system based on the single input interval type-2 fuzzy method

Non-Strict Feedback (NSF) physical systems are broader than Strict-Feedback (SF) systems. The system state variables depend on their system functions, unlike SF systems that the system variables do not depend on their system function. Consequently, NSF physical systems are challenging to control. To address this control challenge in NSF physical systems, this research proposed an Event-Triggered (ET) adaptive backstepping control method based on Single Input Interval Type-2 Fuzzy (SIIT-2F) for NSF system with input delay. The effect of the method is examined on a third order system using transient and steady state performance as a metric. The ET mechanism prompts due to violation of a predefined condition (mismatch between the actuator and the controller), uncertainty of the system dynamics was handled by the Radial Basis Function Neural Network (RBFNN). The input delay was handled by a developed auxiliary system, and the SIIT-2F was used to minimize error between state variables and auxiliary states of the adaptive backstepping controller. The results of this work were compared with adaptive neural control for NSF method. The method has an average percentage performance improvement of23% peak overshoot for state variables; and 47% for adaptive variables, the average performance improvement for the absolute peak undershoot are 50%, and 60% respectively for the state and auxiliary variables. In addition, the settling time average performance improvement is 11%, 21%, and 10% for state, auxiliary, and adaptive variables, respectively. The results of this study underline that the ET based SIIT-2F controller improved the performance of NSF systems compared to the adaptive neural control method.

Event-Based Distributed Fuzzy Load Frequency Control for Multiarea Nonlinear Power Systems With Switching Topology

This article investigates the event-based distributed fuzzy load frequency control approach for nonlinear interconnected power systems under a switching topology. Specifically, nonlinear factors and uncertain parameters existing in turbine and governor dynamics are modeled and analyzed under the interval type-2 fuzzy method. New memory-based adaptive event-triggered mechanisms, whose directions and threshold parameters are dynamically updated based on the output change trend and relative output error of multiple memories, are proposed to further save network energy. Aiming at communication topology changes in multiarea power systems, a novel switched control system is established by introducing a time-varying communication matrix. Then, the system’s asymptotic stability is ensured by the piecewise Lyapunov–Krasovskii method. Furthermore, to further relax stability conditions, slack matrices are considered with the boundary information of membership functions. Finally, three-area power systems are shown to demonstrate the effectiveness of proposed distributed load frequency control strategies.

Fuzzy Type-2 Trapezoid Methods for Decision Making Salt Farmer Mapping

The need for domestic salt every year has increased, both for consumption and industrial salt. Some of the fisheries service programs include providing assistance to people's businesses, providing geomembrane, and online marketing training. A large number of salt farmers and official work programs have caused the implementation of the program to be less than optimal, resulting in low salt production. This study uses a type-2 fuzzy method by integrating two methods, namely type-2 Fuzzy Analytical Hierarchy Process AHP (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Fuzzy type-2 has higher accuracy than fuzzy type-1 and is more efficient and more flexible in determining the linguistic scale for criteria. The Fuzzy Analytical Hierarchy Process AHP (FAHP) interval is used to determine the weight of the salt farmer mapping criteria. Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), used to determine. The findings of this study are that the indicators that most influence the mapping of salt farmers are land area, marketing, and market. The results of the mapping of salt farmers are the classification of salt farmer class groups and recommendations for improvement for each salt farmer. Hybrid type-2 Fuzzy Analytical Hierarchy Process AHP (FAHP) method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), can be used for mapping salt farmers based on the consistency ratio value below 10 percent, 37 percent enter high class, 28 percent enter the middle class and 35 percent enter low class

Observer-based type-2 fuzzy approach for robust control and energy management strategy of hybrid energy storage systems

In this paper, an observer-based type-2 fuzzy method is proposed for control and energy management strategy (EMS) of the hybrid energy storage system (HESS) which can be composed of the fuel cell (FC), battery (BA), and supercapacitor (SC). The objective and main contribution of the suggested strategy is to provide: 1) Appropriate tracking performance of power sources by an observer-based control method in the presence of noise and signal ripples. 2) An observer-based composite adaptive type-2 fuzzy (OCAT2F) to approximate the voltage of power sources. 3) A dynamical model of DC-bus to guarantee the stability of closed-loop system. 4) An intelligent EMS. To have a high-power supply, the proposed EMS includes two parts; a type-2 fuzzy logic control rule table (T2FLCRT), and an observer-based robust adaptive fuzzy type-2 fuzzy (ORAT2F). Furthermore, stability analyses of the closed-loop system are provided by the input-output linearization (I-OL) approach and based on the Lyapunov theorem. The simulation results of the proposed control scheme under MATLAB/Simulink indicate that the suggested strategy can provide a suitable control performance, and stability of the whole system is achieved.

Developing a holistic fuzzy hierarchy-cloud assessment model for the connection risk of renewable energy microgrid

Grid-connection of renewable energy microgrids (GCREM) is an important form of promoting the use of clean and renewable energy (RE). However, GCREM will bring huge risks to the power grid. Accurate evaluation and control of risks are critical to the development of GCREM. The existing research is not deep and comprehensive enough to make a reliable evaluation on the risks. The current study proposes a new evaluation framework based on the unit decomposition of the system of GCREM, and then builds a holistic fuzzy hierarchy-cloud assessment model for the risks. The main contributions of this paper are as follows: (1) decomposes GCREM risks to each functional unit of the system, and clarifies the risk transmission between the units within the system; (2) improves the accuracy of weighting risk variables based on interval type-2 fuzzy method; (3) realizes the visibility of risk evaluation and a prior judgment on its effectiveness; (4) establishes a 5-dimensional risk evaluation system of GCREM for the first time, which achieved full coverage of risk variables without the overlap between dimensions. The empirical analyses show that our model can not only assess the overall risk level of GCREM, but also identify key risk sources.

An Improved General Type-2 Fuzzy Approach for Planning Energy Resources Systems with Air Quality Requirements

Due to economic development and the improvement of people’s living standards, questions that the imbalance of energy supply and demand as well as the environmental damage have become increasingly prominent. To study the contradiction between energy and environment, and considering there are a lot of uncertain factors in the actual planning, this paper improves a chance-constrained interval type-2 fuzzy method used in linear programming and extends the interval type-2 fuzzy number to the general type-2 fuzzy number by introducing the secondary membership function that makes up for the loss of uncertainty due to the fixed value of the second membership function and optimizes the accuracy of the probability in the original method. By applying the new method to the energy planning system model, an optimal planning plan with the goal of reducing air pollutant emissions as well as the lowest overall operating cost can be obtained. Compared with the interval fuzzy method, the new method proposed in this paper is able to deal with the uncertainty in the system better, and provides more accurate solutions under different satisfaction levels.

Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations

Electric vehicles are the key to facilitating the transition to low-carbon ‘green’ transport. However, there are concerns with their range and the location of the charging stations which delay a full-fledged adoption of their use. Hence, the electric charging infrastructure in a given region is critical to mitigating those concerns. In this study, an interval type-2 fuzzy set based multi-criteria decision-making method is introduced for selecting the best location for electric charging stations. This method is improved by Simulated Annealing obtaining the best configuration of the parameters of the interval type-2 membership functions along with two different aggregation operators; linguistic weighted sum and average. The proposed overall reusable multi-stage solution approach is applied to a real-world public transport problem of the municipal bus company in Istanbul. The results indicate that the approach indeed improves the model, capturing the associated uncertainties embedded in the interval type-2 membership functions better, leading to a more effective fuzzy system. The experts confirm those observations and that Simulated Annealing improved interval type-2 fuzzy method achieves more reliable results for selecting the best sites for the electric bus charging stations.

Precision Trajectory Tracking on XY Motion Stage Using Robust Interval Type-2 Fuzzy PI Sliding Mode Control Method

Precision contour tracking is one of the most important factors used to determine product quality in a machining tool. An interval type-2 fuzzy proportional–integral (PI) sliding mode control (IT2FPISMC) system is proposed herein to control the mover position of the two-axis motion stage with optical encoder sensors for trajectory feedback. A type-2 fuzzy method that can handle rule uncertainties is developed to approach the unknown nonlinear systems. The PI term is used to approximate the discontinuous control signal and mitigate the chattering phenomenon in the presence of unmodeled system dynamics and external disturbances. The adaptive control laws are derived based on the Lyapunov theorem, such that the closed-loop stability is guaranteed, and the output tracking errors of the system asymptotically converge to zero. A non-uniform rational B-spline interpolator with high accuracy is adopted in the biaxial linear stage. Moreover, typical circular, bowknot, heart, and star reference contours are tested. The results on the average tracking error and the tracking error standard deviation are experimented and compared to illustrate the performance of our proposed method. The tracking performance obtained from the IT2FPISMC method is better than that of the conventional method. Furthermore, the proposed method can achieve robustness for tracking different reference contours in industrial applications.

Different methods to fuzzy -R control charts used in production

PurposeThe control charts are used in many production areas because they give an idea about the quality characteristic(s) of a product. The control limits are calculated and the data are examined whether the quality characteristic(s) is/are within these limits. At this point, it may be confusing to comment, especially if it is slightly below or above the limit values. In order to overcome this situation, it is suitable to use fuzzy numbers instead of crisp numbers. The purpose of this paper is to demonstrate how to create control limits of -R control charts for a specified data set of interval type-2 fuzzy sets.Design/methodology/approachThere are methods in the literature, such as defuzzification, distance, ranking and likelihood, which may be applicable for interval type-2 fuzzy set. This study is the first that these methods are adapted to the -R control charts. This methodology enables interval type-2 fuzzy sets to be used in -R control charts.FindingsIt is demonstrated that the methods – such as defuzzification, distance, ranking and likelihood for interval type-2 fuzzy sets – could be applied to the -R control charts. The fuzzy control charts created using the methods provide similar results in terms of in/out control situations. On the other hand, the sample points depicted on charts show similar pattern, even though the calculations are different based on their own structures. Finally, the control charts obtained with interval type-2 fuzzy sets and the control charts obtained with crisp numbers are compared.Research limitations/implicationsBased on the related literature, research works on interval type-2 fuzzy control charts seem to be very limited. This study shows the applicability of different interval type-2 fuzzy methods on -R control charts. For the future study, different interval type-2 fuzzy methods may be considered for -R control charts.Originality/valueThe unique contribution of this research to the relevant literature is that interval type-2 fuzzy numbers for quantitative control charts, such as -R control charts, is used for the first time in this context. Since the research is the first adaptation of interval type-2 fuzzy sets on -R control charts, the authors believe that this study will lead and encourage the people who work on this topic.

An Integrated TOPSIS, GRA and VIKOR Based on Interval Type-2 Fuzzy Method to Improve Customer Satisfaction in Public Transportation

Recently, multi-criteria decision making problems with interval type-2 fuzzy methods have received increasing attention both from researchers and practitioners. In this study, an interval type-2 fuzzy TOPSIS and GRA based VIKOR method is proposed for the evaluation of the customer satisfaction in all the transportation modes in Istanbul (metro, bus and bus rapid transit). Furthermore, the interval type-2 fuzzy TOPSIS method is also utilized to solve the problem. An online survey is conducted to investigate factors affecting public transport users’ satisfaction with the service. Data is collected from 323 public transport users in Istanbul. As a result, an interval type-2 fuzzy multi-criteria decision making method has been proposed for the evaluation of customer satisfaction in public transportation. The performances of various multi-criteria decision making methods are also compared with each other with a view to exploring the effectiveness and flexibility of proposed method and interval type-2 fuzzy TOPSIS method. The results show that the proposed method is reliable and practical for evaluate problems and other MCDM problems.

Fuzzy based finger vein recognition with rotation invariant feature matching

Finger vein recognition is a promising biometric with commercial applications which is explored widely in the recent years. In this paper, a finger vein recognition system is proposed using rotation invariant feature descriptors for matching after enhancing the finger vein images with an interval type-2 fuzzy method. SIFT features are extracted and matched using a matching score based on Euclidian distance. Rotation invariance of the proposed method is verified in the experiment and the results are compared with SURF matching and minutiae matching. It is seen that rotation invariance is verified and the poor quality issues are solved efficiently with the designed system of finger vein recognition during the analysis. The experiments underlines the robustness and reliability of the interval type-2 fuzzy enhancement and SIFT feature matching.

THE REGULATION OF THE BLOOD GLUCOSE LEVELS BY TYPE-2 FUZZY CONTROLLER

It is essential to control accurately the critical variables in patients whose normal control systems have been faced with trouble. Vital parameters were different for different people; also with respect to similar changes in the parameters, the responses were different. This issue led to changes in the model parameters. It should be noted that even a small change in some of the model parameters can lead to death of patients. Therefore, the purpose of this study was to design a controller which was robust with respect to model parameter changes and disturbances entered to the system; this controller must be able to control their blood sugar levels in a suitable settling time. One of the issues discussed in this paper is type-2 fuzzy method, which is to regulate blood glucose levels. The use of type-2 fuzzy systems allows us to model the effects of uncertainty in the systems which are based on special rules. It also gives us an opportunity to minimize the uncertainty effects, but unfortunately, because of the complexity in use and comprehension of type-2 fuzzy sets than type-1, they were less used. These complexities are due to its three-dimensional nature and the direct dependence of its relations to the development principles; this is how its computational complexity is made.

Type2 Fuzzy Soft Computing Technique for Image Enhancement

<p class="Default">The mainpurpose of Image enhancement is to process an image so that outcome is more appropriate than original image for definite application. The fuzzy logic isone of the soft computing techniques to enhance the images by eliminating uncertainty.In this paper efficient type2 fuzzy logic technique is used to get betterquality image. This method consists of two steps. In the First step fisher criterion function is useful to generate type1 fuzzy membership value. In the second step based on type1 membership value fuzzy rules are derived to enhance the image. The type2 fuzzy method is compared with type1 fuzzy. The table values and graphs provethat the proposed method gives better results compared with fuzzy type1 method.</p>

Data-driven modeling and optimization of thermal comfort and energy consumption using type-2 fuzzy method

In the research domain of intelligent buildings and smart home, modeling and optimization of the thermal comfort and energy consumption are important issues. This paper presents a type-2 fuzzy method based data-driven strategy for the modeling and optimization of thermal comfort words and energy consumption. First, we propose a methodology to convert the interval survey data on thermal comfort words to the interval type-2 fuzzy sets (IT2 FSs) which can reflect the inter-personal and intra-personal uncertainties contained in the intervals. This data-driven strategy includes three steps: survey data collection and pre-processing, ambiguity-preserved conversion of the survey intervals to their representative type-1 fuzzy sets (T1 FSs), IT2 FS modeling. Then, using the IT2 FS models of thermal comfort words as antecedent parts, an evolving type-2 fuzzy model is constructed to reflect the online observed energy consumption data. Finally, a multiobjective optimization model is presented to recommend a reasonable temperature range that can give comfortable feeling while reducing energy consumption. The proposed method can be used to realize comfortable but energy-saving environment in smart home or intelligent buildings.

Disease - Related Gene Identification by an Improved Type - 2 Fuzzy Method on Microarrays

Type-2 fuzzy set can control the uncertainty information more effectively than conventional type-1 fuzzy set because its membership function is three-dimensional. In this paper we perform the identification of disease-related genes based on DNA microarray by type-2 fuzzy set theory and established type-2 fuzzy membership function to describe the differences of the gene expression values generated form normal people’s genes and patient’s. We also make a comparison between our type-2 fuzzy method with the traditional fuzzy method. The results suggest the type-2 FM-d values are significantly different, which make the identifications more reasonable and convincing than the original one.

A Type-2 Fuzzy Method for Identification of Disease-related Genes on Microarrays

Fuz zy set theory has been widely used in the analysis of gene microarray data. However, due to noise and uncertainty inherent in microarray data, traditional fuzzy methods sometimes do not perform well. In this paper, we propose a type-2 fuzzy membership test (Type-2 FM test) for disease-associated gene identification on microarrays to improve traditional fuzzy methods. We apply this method on diabetes and lung cancer microarrays and make a comparison with traditional fuzzy methods. For diabetes data, we can identify 7 genes which have been confirmed to be related to diabetes treatment in the published literature and one more gene can be identified than original approaches. For lung cancer data, we can also identify 7 genes which have been confirmed to be associated with lung cancer treatment in published literature and the type-2 d-values are significantly different. The results show that our type-2 FM test performs better than traditional fuzzy methods when analyzing microarray data with similar expression values and noise.

Web shopping expert using new interval type-2 fuzzy reasoning

Finding a product with high quality and reasonable price online is a difficult task due to uncertainty of Web data and queries. In order to handle the uncertainty problem, the Web Shopping Expert, a new type-2 fuzzy online decision support system, is proposed. In the Web Shopping Expert, a fast interval type-2 fuzzy method is used to directly use all rules with type-1 fuzzy sets to perform type-2 fuzzy reasoning efficiently. The parameters of type-2 fuzzy sets are optimized by a least square method. The Web Shopping Expert based on the interval type-2 fuzzy inference system provides reasonable decisions for online users.