Interval Type-2 Fuzzy Approach Research Articles

Multi-criteria solar power plant siting problem solution using a GIS-Taguchi loss function based interval type-2 fuzzy approach: The case of Kars Province/Turkey

The determination of the areas where the solar power plant will be installed is of great importance for the performance of the solar power plant. Solar and hydroelectric energy are the most widely used renewable energy sources in Kars province. Site selection for these power plants is an important factor in terms of reducing the installation cost of the solar power plant and achieving maximum efficiency during operation. Determining the areas where the power plants will be installed is a very complex and difficult to analyse spatial decision making problem. In this study, firstly GIS is used as a mapping method to obtain the locations of both solar power plants in Susuz, Arpaçay, Akkaya, Kars city centre, Selim, Digor, Kağızman and Sarıkamıș districts of Kars province and then Taguchi loss function based interval type-2 fuzzy approach is applied to the problem. In order to obtain more accurate results, the results of the two methods (GIS and Taguchi loss function based interval type-2 fuzzy approach) were also compared. According to the solar power plant map obtained, it was determined that the total area of suitable areas is 78600 km2.

Composite fault reconstruction and fault-tolerant control design for cyber-physical systems: An interval type-2 fuzzy approach

This article scrutinizes the stabilization and fault reconstruction issues for interval type-2 fuzzy-based cyber–physical systems with actuator faults, deception attacks and external disturbances. The primary objective of this research is to formulate the learning observer system with the interval type-2 fuzzy technique that reconstructs the actuator faults as well as the immeasurable states of the addressed fuzzy based model. Further, the information of reconstructed actuator faults is incorporated in the developed controller with the imperfect premise variables for ensuring the stabilization of the system under consideration. At the same time, the H∞ technique is employed to reduce the impact of external disturbances in the considered model. In addition to that, the deception attacks are represented as a stochastic variable that satisfies the Bernoulli distributions. On the ground of this, a set of sufficient criteria is deduced in the context of linear matrix inequalities to affirm the stability of the addressed systems. Furthermore, the requisite gain matrices are computed by resolving the obtained linear matrix inequality based stability criteria. At last, two simulation examples, including the mass–spring–damper system are exhibited to demonstrate the usefulness of analytical findings of the developed strategy.

Admissible Control for Non-Linear Singular Systems Subject to Time-Varying Delay and Actuator Saturation: An Interval Type-2 Fuzzy Approach

Applied in many fields, nonlinear systems involving delay and algebraic equations are referred to as singular systems. These systems remain challenging due to saturation constraints that affect actuators and cause harm to their operation. Furthermore, the complexity of the problem will increase when uncertainty also simultaneously affects the system under consideration. To address this issue, this paper investigated a feasible control strategy for nonlinear singular systems with time-varying delay that are subject to uncertainty and actuator saturation. The IT-2 fuzzy model was adopted to describe the dynamic of the non-linear delayed systems using lower and upper membership functions to deal with the uncertainty. Moreover, the polyhedron model was applied to characterize the saturation function. The goal of the control approach was to design a relevant IT2 fuzzy state feedback controller with mismatched membership functions so that the closed-loop system is admissible. On the basis of an appropriate Lyapunov–Krasovskii functional, sufficient delay-dependent conditions were established and an optimization problem was formulated in terms of linear matrix inequality constraints to optimize the attraction domain. Simulation examples are provided to verify the effectiveness of the proposed method.

Interval Type-2 Fuzzy Approach for Dynamic Parameter Adaptation in the Bird Swarm Algorithm for the Optimization of Fuzzy Medical Classifier

Optimization is essential for applications since it can improve the results provided in different areas; for this task, it is beneficial to use soft computing techniques, such as bio-inspired algorithms. In addition, it has been shown that if dynamic parameter adaptation is applied to these algorithms, they can provide a better result. For this work, the main contribution is to carry out the dynamic parameter adaptation to the bird swarm algorithm using interval type-2 fuzzy systems to realize a new fuzzy bio-inspired algorithm. The design of the proposed fuzzy system consists of two inputs corresponding to the iterations and diversity. As outputs, it takes the values of C and S, which are parameters to be adjusted by the algorithm. Once the design and the experimentation are realized, they are divided into two study cases. The first consists of a set of complex functions of the Congress of Evolutionary Competition 2017. The second case study consists of optimizing the membership functions in a fuzzy system designed to provide the nocturnal blood pressure profile, which corresponds to a neuro-fuzzy hybrid model to obtain the risk of hypertension. Analyzing the 30 experiments performed in both case studies, we can observe that the results obtained are improved when compared with the original method and other proposed methodologies, achieving good results in the complex functions. In addition, the optimized fuzzy system will reach an average of 97% correct classification. Statistically, it can be concluded that there is significant evidence to affirm that the proposed method provides good results.

Asynchronous Fault Detection for Interval Type-2 Fuzzy Nonhomogeneous Higher Level Markov Jump Systems With Uncertain Transition Probabilities

Based on the interval type-2 fuzzy (IT2F) approach, this article investigates the fault detection filter design problem for a class of nonhomogeneous higher level Markov jump systems with uncertain transition probabilities. Considering that the mode information of the system cannot be obtained synchronously by the filter, the hidden Markov model can be seen as a detector to handle this asynchronous problem, and the parameter uncertainty can be processed by the IT2F approach with the lower and upper membership functions. Then, the asynchronous IT2F filter is designed to deal with the fault detection problem. Furthermore, the Gaussian transition probability density function is introduced to describe the uncertainty transition probabilities of the system and the filter. Based on the Lyapunov theory, the existence of the designed asynchronous IT2F filter and the dissipativity of the filter error system can be well ensured. In this article, the simulation study on a quarter-car suspension system verifies that the designed asynchronous IT2F filter can detect faults without error alarms.

A new interval type-2 fuzzy approach for multi-scenario project cash flow assessment based on alternative queuing method and dependency structure matrix with a case study

Managing project costs effectively, which comprises numerous aspects, is a highly complicated process for project managers that needs dependable cash knowledge in the entire life cycle of the project. Project managers’ proficiency and anticipating dependable cash flows lead to substantial improvements in project costs management. Moreover, healthy cash flow prognostication for the whole construction project gives project managers a real insight into the identification of problems, and its deficiency results in the failure of firms even with big profits to maintain themselves as well. Therefore, predicting reliable cash flow and monitoring the progress of projects in terms of cash are essential to be considered. Furthermore, due to the existence of uncertainties in projects, it is necessary to find it as a significant part of different approaches. Building upon these, an innovative approach is presented in this paper to prognosticate project cash flow based on both type-2 fuzzy extension of dependency structure matrix for project scheduling with overlapping activities and extended alternative queuing method under type-2 fuzzy environment to adopt the best scenario. Moreover, type-2 fuzzy numbers are applied to address the uncertainty of activities. Subsequently, a real case study of a gas field development project is employed to represent the efficiency of proposed approach. Ultimately, a comparative study is conducted to validate the results, and superiorities of the proposed approach are addressed from different aspects. The results illustrate that the presented approach is capable of handling real project problems and providing project managers with a suitable tool to comprehend the cash conditions of the projects.

An innovative interval type-2 fuzzy approach for multi-scenario multi-project cash flow evaluation considering TODIM and critical chain with an application to energy sector

Project management has been proven to be an effective tool to manage sophisticated activities. Various techniques of project management have played an essential role for successful project implementation in different areas. Managing projects, especially in a multi-project environment, involves a complex situation because of its distinguishing feature in which a number of projects are being executed simultaneously, that is, they are followed in parallel. Therefore, applying human resources will be more effective and more idle time will be eliminated as well. More so, it can enable people to share their lessons learned from one project to another. With respect to handling a number of projects at the same time by most firms, it is sophisticated for contractors to cope with financial issues of projects, which involve different project cash inflows and outflows. Thus, taking an accurate cash flow prediction into account for projects has been changed into a crucial matter for firms, and lack of this consideration may result in the failure of projects as well. Moreover, there is a desperate need for uncertainties to be addressed thoroughly regarding their vital role for suitable project management. With these in mind, an innovative approach is proposed in this paper to anticipate the cash flow of project by considering type-2 fuzzy extension of both critical chain project management (CCM) for project scheduling and TODIM (an acronym in Portuguese for interactive Multi-Criteria Decision Making) method for selecting the best scenario in a multi-project environment. Hence, type-2 fuzzy numbers are utilized in order to state uncertainties. Eventually, a real-world project in a petro-refinery firm is applied to indicate the capability of the presented approach.

Fault Detection for Fuzzy Semi-Markov Jump Systems Based on Interval Type-2 Fuzzy Approach

This article studies the fault detection problem for continuous-time fuzzy semi-Markov jump systems (FSMJSs) by employing an interval type-2 (IT2) fuzzy approach. First, the continuous-time FSMJSs model is designed and the parameter uncertainty is addressed by the IT2 fuzzy approach, where the characteristic of sensor saturation is taken into account in the control system. Second, the IT2 fuzzy semi-Markov mode-dependent filter is constructed, which is employed to deal with the fault detection problem. Then, by using the Lyapunov theory, it can be guaranteed that the constructed fault detection model based on this filter and IT2 FSMJSs is stochastically stable with H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance. Moreover, the quantization strategy is applied to the fault detection plant to dispose of the problem of limited network bandwidth. Compared with the existing literature, the differences mainly lie in two aspects, one is that the IT2 fuzzy method is utilized for FSMJSs to tackle the parameter uncertainty of system, and the other is to detect the fault signal of IT2 FSMJSs by using the fault detection system that is constructed based on the IT2 fuzzy semi-Markov mode-dependent filter and IT2 FSMJSs. Finally, two simulation examples are provided to illustrate the effectiveness and the usefulness of the proposed theoretical method.

A new interval type-2 fuzzy approach for analyzing and monitoring the performance of megaprojects based on earned value analysis (with a case study)

Major factors of project success include using tools of performance measurements and feedbacks. Earned value management (EVM) is a unique issue within megaprojects due to their inevitable external risks and variations. In order to improve the effectiveness and accuracy of future status estimation of megaprojects, in this paper a novel evaluation model is proposed which takes account of interval type-2 fuzzy sets (IT2FSs) to cope with uncertainty. In the proposed approach, in addition to cost and time criteria, a great deal of attention is paid for other important factors affecting project success, including quality, stakeholder satisfaction, safety and risk, which is computed from different perspectives. Moreover, to make informed decisions and to reduce uncertainty in assessment of megaprojects, key performance indicators (KPIs) are provided. Also, a new extension of multi-criteria decision-making method is introduced to weigh KPIs in future performance equations. Finally, the proposed IT2F-EVM approach is applied to control and estimate the future status of a real case study in a petro-refinery company. The results show that the approach can successfully address highly uncertain environments.

Uncertain fuzzy self-organization based clustering: interval type-2 fuzzy approach to adaptive resonance theory

Conventional unsupervised learning algorithms require knowledge of the desired number of clusters beforehand. Even if such knowledge is not required in advance, empirical selection of the parameter values may limit the adaptive capability of the algorithm, thereby restricting the clustering performance. An inherent uncertainty in the number and size of clusters requires integration of fuzzy sets into a clustering algorithm. In this paper, we propose a type-1 (T1) fuzzy ART method that adaptively selects the vigilance parameter value, which is critical in determining the network dynamics. This results in improved clustering performance due to the added flexibility in dynamic selection of the number of clusters with the use of fuzzy sets. To further manage the uncertainty associated with memberships, we extend the proposed T1 fuzzy ART with adaptive vigilance to an interval type-2 (IT2) fuzzy ART method. Type reduction and defuzzification are then performed using the KM algorithm to obtain a defuzzified vigilance parameter value. We evaluate our proposed methods on several data sets to validate their effectiveness.

Type-2 fuzzy adaptive sliding mode control of hypersonic flight

This paper proposes a type-2 fuzzy adaptive sliding mode control scheme for tracking control of hypersonic aircraft with uncertainties. This method uses full-state feedback to linearize the nonlinear model of hypersonic aircraft. Combining the interval type-2 fuzzy approach and adaptive sliding mode control keeps the system stable in the existence of uncertain parameters. For rapid stabilization of the system, the adaptive laws are designed using a direct constructive Lyapunov analysis together with a well-established type-2 fuzzy logic control. Simulation test results indicate that the proposed control scheme provides enhancement of robustness to parametric uncertainty and improvement in tracking performance of the hypersonic aircraft.

A narrow band interval type-2 fuzzy approach for image segmentation

With the extension of traditional fuzzy sets into interval type-2 fuzzy sets, uncertain knowledge or information of an image will be well described by uncertain membership values.Pixels within a small selected region are updated at each iteration, which enables the contour to evolve gradually and reduces the computational cost on the employment of the interval type-2 fuzzy set.The spatial constraint providing more image details has been intensively used in image segmentation algorithms. In the proposed model, the weighted fuzzy factor which takes both spatial and gray constraints into consideration is designed for computing the type-2 fuzzy membership value. Traditional fuzzy sets capture vagueness through precise numeric membership degrees. This poses a dilemma of excessive precision in describing uncertain phenomenon. Interval type-2 fuzzy sets have shown its effectiveness in handling uncertainties in comparison to the traditional fuzzy sets. In this paper, the interval type-2 fuzzy approach is introduced into the framework of active contour model, which effectively segment images with large uncertainties. However, the computational cost is largely increased by employing the interval type-2 fuzzy set. Therefore, we try to update the pixels within a narrow band region near the contour boundary for reducing the computational cost caused by employing the interval type-2 fuzzy set. Moreover, both spatial and gray constraints are taken into consideration when calculating the fuzzy membership value to retain more image details. Experimental results on synthetic and real images show that the proposed method is effective and efficient, and is relatively independent of initial conditions.

An interval type-2 fuzzy approach for real-time EEG-based control of wrist and finger movement

Abstract Feature extraction and automatic classification of mental states is an interesting and open area of research in the field of brain–computer interfacing (BCI). A well-trained classifier would allow the BCI system to control an external assistive device in real world problems. Sometimes, standard existing classifiers fail to generalize the components of a non-stationary signal, like Electroencephalography (EEG) which may pose one or more problems during real-time usage of the BCI system. In this paper, we aim to tackle this issue by designing an interval type-2 fuzzy classifier which deals with the uncertainties of the EEG signal over various sessions. Our designed classifier is used to decode various movements concerning the wrist (extension and flexion) and finger (opening and closing of a fist). For this purpose, we have employed extreme energy ratio (EER) to construct the feature vector. The average classification accuracy achieved during offline training and online testing over eight subjects are 86.45% and 78.44%, respectively. On comparison with other related works, it is shown that our designed IT2FS classifier presents a better performance.

Uncertain Fuzzy Clustering: Insights and Recommendations

In this article, interval type-2 fuzzy sets were used to model the uncertainty that is associated with the various parameters in objective function-based clustering. The purpose was to represent and manage the uncertainty in the cluster memberships by incorporating interval type-2 fuzzy sets. As a result, interval type-2 clustering methods were obtained by modifying the prototype-updating and hard-partitioning procedures in the type-1 fuzzy objective function-based clustering. As a consequence, the management of uncertainty by an interval type-2 fuzzy approach aids cluster prototypes to converge to a more desirable location than a type-1 fuzzy approach. Several examples illustrated the effectiveness of interval type-2 fuzzy approach methods. Furthermore, the uncertainty associated with the parameters for other existing clustering algorithms can be considered in the development of several other interval type-2 clustering algorithms. They are currently under investigation