Type-2 Fuzzy System Research Articles

Type-2 fuzzy-logic based control of photovoltaic-hydrogen production systems

Green hydrogen will play an important role for sustainable development, it can be considered as a promising solution for energy transition. In this paper, direct and indirect-coupled configuration of hydrogen production system consisted of Photovoltaic (PV) source and electrolyzer stack are investigated. A study on power loss and efficiency in PV-electrolyzer system is conducted for both cases. Optimal matching conditions in directly coupled hydrogen system can be achieved by modifying the configuration of series/parallel cells in electrolyzer stack or PV generator. However, the energy transfer can be improved by incorporating a buck converter between PV string and electrolyzer stack. In this article, the type-2 fuzzy inference system is proposed to control the operating point of PV string by adjusting the duty cycle of control signal of buck converter. A comparison with classical MPPT methods is carried out in order to verify the efficiency of suggested technique. It has been found that the indirect-coupled hydrogen system can achieve higher energy transfer rate by ensuring continuous extracting of maximum power of PV source under various operating conditions. The obtained results, under Matlab/Simulink environment, demonstrated a satisfactory performance of type-2 fuzzy logic controller in both transient and permanent regimes. We obtain a minimal power loss value (less than 8.1%) in directly-coupled hydrogen production system, in addition a satisfactory value of efficiency (more than 93%) has been obtained.

A new type of fuzzy systems in terms of vague partitions

This paper focuses on simplifying the structure of fuzzy systems and improving the precision. By regarding the fuzzy rule base as a mapping from the vague partition on the input universe to the vague partition on the output universe, we first design a new type of fuzzy system using the complete and continuous fuzzy rule base in terms of vague partitions. We then exploit Weierstrass’s approximation theorem to show that this new type of fuzzy system can approximate any real continuous function on a closed interval to arbitrary accuracy and provide the corresponding approximation accuracy with respect to infinite norms. We also provide two numerical examples to illustrate the effectiveness of this new type of fuzzy system. Both theoretical and numerical results show that this new type of fuzzy system achieves the quite approximation effect with a few fuzzy rules.

IoT-based patient stretcher movement simulation in smart hospital using type-2 fuzzy sets systems

The healthcare facilities in the modern age are the key challenge in developing countries where remote areas lack immense-quality hospitals and medical experts. The idea here is to provide an intelligent smart health system using some sensors and fuzzy controllers which are implemented in stretchers, and the update of the patient stretcher monitoring is implemented to monitor the immovable stretchers and rectify the problem of immovable stretchers, which can be solved by Type-2 fuzzy sets and systems, which is featured by three-dimensional characteristics. This study proposes IoT-based Patient Stretcher Movement Simulation for Smart Hospitals using Type-2 fuzzy sets and systems. Type-2 fuzzy logic is a generalisation of conventional fuzzy logic in which the stretcher movement simulation and exact location of the stretcher are monitored by the sensor-based fuzzy set and let the model minimise the effects of uncertainties which is found to have very wide applicability in rule-based fuzzy logic systems. The results presented in this paper prove the concept and provide the IoT-based patient stretcher creation. The results of the experiments also show that the proposed approach is more reliable and intelligent enough to provide efficient monitoring applications through the type-2 fuzzy logic systems.

Design and development of brain emotional learning based adaptive membership functions for type-2 fuzzy systems

This paper presents the design and development of Brain Emotional Learning based adaptive Type-2 Fuzzy Systems for control of dynamical systems. The BEL controller belongs to the class of bio inspired controllers, as its architecture is based on limbic system of human brain and is capable of providing solutions for complex real time problems. In this work, dynamics of Brain Emotional Learning are used for the adaptation of membership functions in the design of Type-2 Fuzzy Logic Controllers. The stability of the overall system is analysed through Lyapunov Yakubovich’s criteria. The proposed approach is validated on the benchmark system such as inverted pendulum, CSTR and Ship heading control through simulation and in real-time environment using OPAL RT OP5600.

Antidisturbance Control Design for Interval Type-2 Fuzzy Stochastic Systems With Input Quantization

Quantized anti-disturbance control design problem is presented for a class of interval type-2 (IT2) fuzzy stochastic systems subject to time delays and multiple disturbances. The inherent uncertain nonlinear and hybrid characteristics of the concerned system make it difficult to design a stable anti-disturbance controller. In order to properly reflect the characteristics of IT2 fuzzy stochastic models with multiple disturbances, a new fuzzy disturbance observer is proposed to estimate the disturbances generated by a fuzzy exogenous system. Furthermore, a quantized fuzzy anti-disturbance control scheme is synthesized by fusing the estimation of the multiple disturbances. With the support of Lyapunov functional method, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$It{\hat{o}}$</tex-math></inline-formula> 's formula and auxiliary function-based integral inequality, the resulting IT2 fuzzy stochastic systems are proved to be robustly stochastically stable with mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> /passivity performance index. The design methods of the fuzzy disturbance observer and quantized anti-disturbance controller are formulated in the form of linear matrix inequalities so that the corresponding gain matrices can be easily obtained. Finally, simulation studies on three examples are provided to justify the efficiency of the designed control strategy.

A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative to classical fuzzy logic. The primary purpose of fuzzy logic is to simulate reasoning in a computer. This work aims to use shadowed type-2 fuzzy systems (ST2-FS) to dynamically adapt the crossing parameter of differential evolution (DE). To test the performance of the dynamic crossing parameter, the motor position control problem was used, which contains an interval type-2 fuzzy system (IT2-FS) for controlling the motor. A comparison is made between the original DE and the algorithm using shadowed type-2 fuzzy systems (DE-ST2-FS), as well as a comparison with the results of other state-of-the-art metaheuristics.

A novel robust extended dissipativity state feedback control system design for interval type-2 fuzzy Takagi-Sugeno large-scale systems

Recently, systems have become large in their model and dynamic. To apply control algorithms, serious problems appear that need to be solved. Two significant problems are modelling the dynamics of the large-scale system and reducing effects of perturbations. In this paper, we use the advantage of large-scale systems modelling based on the type-2 fuzzy Takagi–Sugeno model to cover the uncertainties caused by large-scale systems modelling. The advantage of using membership function information is the reduction of conservatism resulting from stability analysis. Also, this paper uses the extended dissipativity robust control performance index to reduce the effect of external perturbations on the large-scale system, which is a generalization of , , passive and dissipativity performance indexes and control gains can be achieved through solving linear matrix inequalities (LMIs). Hence, the whole closed-loop system is asymptotically stable. Finally, the effectiveness of the proposed method is demonstrated by two practical examples.

Fetal Health State Detection Using Interval Type-2 Fuzzy Neural Networks.

Diagnosis of fetal health is a difficult process that depends on various input factors. Depending on the values or the interval of values of these input symptoms, the detection of fetal health status is implemented. Sometimes it is difficult to determine the exact values of the intervals for diagnosing the diseases and there may always be disagreement between the expert doctors. As a result, the diagnosis of diseases is often carried out in uncertain conditions and can sometimes cause undesirable errors. Therefore, the vague nature of diseases and incomplete patient data can lead to uncertain decisions. One of the effective approaches to solve such kind of problem is the use of fuzzy logic in the construction of the diagnostic system. This paper proposes a type-2 fuzzy neural system (T2-FNN) for the detection of fetal health status. The structure and design algorithms of the T2-FNN system are presented. Cardiotocography, which provides information about the fetal heart rate and uterine contractions, is employed for monitoring fetal status. Using measured statistical data, the design of the system is implemented. Comparisons of various models are presented to prove the effectiveness of the proposed system. The system can be utilized in clinical information systems to obtain valuable information about fetal health status.

Self-learning interval type-2 hierarchical fuzzy system based on rule relevance with online regression prediction application

In this paper, a novel self-learning design of an interval type-2 hierarchical fuzzy system (IT2 HFS) based on rule relevance is proposed. Different from the existing methods, this paper uses data stream instead of batch data to construct the IT2 HFS. As time goes on, HFS gradually fills its rule base through fuzzy partition algorithm, where the rule base of HFS is empty at the beginning. A new self-updating method for consequent parameters is proposed based on rule relevance. When HFS is used for online learning, the proposed method can ensure that the system has better performance. The designed self-learning IT2 HFS is applied to online regression prediction issue, and is tested on two different online regression datasets. Self-learning type-1 HFS is also compared with self-learning IT2 HFS in prediction accuracy and system complexity. The results illustrate that the self-learning IT2 HFS performs better in prediction accuracy with lower rules.

Multiobjective Optimization Design of Interpretable Evolutionary Fuzzy Systems With Type Self-Organizing Learning of Fuzzy Sets

This paper proposes a new multiobjective optimization approach for designing a self-generated interpretable fuzzy logic system (FLS). The types of fuzzy sets can be constructed automatically by self-organizing method, so as to form a hybrid fuzzy system. Different from the existing evolutionary type-1 fuzzy system, which is full of type-1 fuzzy sets, and the evolutionary interval type-2 fuzzy system, which is full of interval type-2 fuzzy sets, there are both type-1 fuzzy sets and interval type-2 fuzzy sets in the hybrid fuzzy system. A new transparency-oriented objective function is defined, and the constraint of the footprint of uncertainty (FOU) of the interval type-2 (IT2) fuzzy set (FS) is considered for the first time. A new FS merging criterion focusing on the proximity of the cores of fuzzy sets is proposed, which is easy to calculate and maintains the characteristics of classical similarity measures. Combined with the new merging criterion, the online cluster and fuzzy set updating (OCFU) algorithm is employed to initialize the reference rule base and the type of fuzzy sets, as it is assumed that no training data are collected in advance. Based on the reference rule base, the advanced multiobjective front-guided continuous ant colony optimization (AMO-FCACO) algorithm is introduced to optimize all the free parameters of the FLS. With the operation mentioned above, the self-generated FLSs achieve a good balance between interpretability and performance. The effectiveness of the proposed method is verified by three nonlinear system tracking problems.

Optimal control of jacket platforms vibrations under the simultaneous effect of waves and earthquakes considering fluid-structure interaction

Offshore platforms are very important structures that experience different types of dynamic loads throughout their service lives. In this study, the effects of the simultaneous occurrence of earthquakes and wave loads are investigated. The influences of earthquake-related water shaking on microwaves have also been considered. For this purpose, a case study is performed on the Ressalat oil platform in the Persian Gulf. The modified endurance wave analysis (MEWA) is used to produce waves corresponding to the structure's location. Moreover, the interactions between the fluid and the structure (FSI), as well as the effect of the added mass caused by the oscillation of the platform in the fluid are considered in the analysis. In order to control platform vibrations, the ideas of semi-active control using magnetic fluid dampers (MR), along with the fractional order proportional-integral-derivative (FOPID) control algorithm, are investigated. For online calculation of the gains of the control algorithm, an observer composed of 2-interval type fuzzy systems (IT2FLS) is used, whose orders, the membership functions of the fuzzy system inputs, as well as the fuzzy rules set, are optimized by Observer-Teacher-Learner-Based Optimization (OTLBO). This technique is a powerful meta-heuristic algorithm. In the used control process, the effect of actuator saturation, which is one of the problems of semi-active control systems, is considered. To evaluate the effectiveness of the proposed control, the maximum value and the root-mean-square (RMS) responses of the platform under 5 waves with different return periods and 7 far-fault earthquake records are compared. The results show that in the cases of simultaneous wave and earthquake loads, the wave load can partly reduce the maximum displacements caused by earthquakes. Also, the maximum and RMS displacement of the jacket platforms' level under wave load with a return period of 100 years and 7 earthquakes are reduced by an average of 35%, which can increase the service life of these structures by 4 times.

Coal Structure Prediction Based on Type-2 Fuzzy Inference System for Multi-Attribute Fusion: A Case Study in South Hengling Block, Qinshui Basin, China

The accurate prediction of coal structure is important to guide the exploration and development of coal reservoirs. Most prediction models are interpreted for a single sensitive coal seam, and the selection of sensitive parameters is correlated with the coal structure, but they ignore the interactions between different attributes. Part of it introduces the concept of the geological strength index (GSI) of coal rocks in order to achieve a multi-element macroscopic description and quantitative characterization of coal structure; however, the determination of coal structure involves some uncertainties among the properties of coal, such as lithology, gas content and tectonic fracture, due to their complex nature. Fuzzy inference systems provide a knowledge discovery process to handle uncertainty. The study shows that a type-2 fuzzy inference system (T2-FIS) with multi-attribute fusion is used to effectively fuse pre-stack and post-stack seismic inversion reservoir parameters and azimuthal seismic attribute parameters in order to produce more accurate prediction results for the Hengling block in the Shanxi area. The fuzzy set rules generated in this paper can provide a more reliable prediction of coal structure in the GSI system. The proposed system has been tested on various datasets and the results show that it is capable of providing reliable and high-quality coal structure predictions.

A New Perspective for Multivariate Time Series Decision Making through a Nested Computational Approach Using Type-2 Fuzzy Integration

The integration of key indicators from the results of the analysis of time series represents a constant challenge within organizations; this could be mainly due to the need to establish the belonging of each indicator within a process, geographic region or category. This paper thus illustrates how both primary and secondary indicators are relevant for decision making, and why they need to be integrated by making new final fuzzy indicators. Thus, our proposal consists of a type-2 fuzzy integration of multivariate time series, such as OECD country risk classification, inflation, population and gross national income (GNI) by using multiple type-1 fuzzy inference systems to perform time series classification tasks. Our contribution consists of the proposal to integrate multiple nested type-1 fuzzy inference systems using a type-2 fuzzy integration. Simulation results show the advantages of using the proposed method for the fuzzy classification of multiple time series. This is done in order so the user can have tools that allow them to understand the environment and generate comparative analyses of multiple information sources, and finally use it during the process prior to decision making considering the main advantage of modeling the inherent uncertainty.

Learning Type-2 Fuzzy Logic for Factor Graph Based-Robust Pose Estimation With Multi-Sensor Fusion

Although a wide variety of high-performance state estimation techniques have been introduced recently, the robustness and extension to actual conditions of the estimation systems have been challenging. This paper presents a robust adaptive state estimation framework based on the Type-2 fuzzy inference system and factor graph optimization for autonomous mobile robots. We use the hybrid solution to connect the advantages of the tightly and loosely coupled technique by providing an inertial sensor and other extrinsic sensors such as LiDARs and cameras. In order to tackle the uncertainty input covariance and sensor failures problems, a learnable observation model is introduced by joining the Type-2 FIS and factor graph optimization. In particular, the use of Type-2 Takagi-Sugeno FIS can learn the uncertainty by using particle swarm optimization before adding the observation model to the factor graph. The proposed design consists of four parts: sensor odometry, up-sampling, FIS based-learning observation model, and factor graph-based smoothing. We evaluate our system by using a mobile robot platform equipped with a sensor setup of multiple stereo cameras, an IMU, and a LiDAR sensor. We imitate the LiDAR odometry in structure environments without needing other bulky motion capture systems to learn the observation model of the visual-inertial estimators. The experimental results are deployed in real-world environments to present the accuracy and robustness of the algorithm.

Event-Triggered Input–Output Finite-Time Stabilization for IT2 Fuzzy Systems Under Deception Attacks

This article addresses the issue of input–output finite-time stabilization for interval type-2 fuzzy systems in the presence of deception attack effects. Our main goal is to make efficient use of network resources by developing an event-triggered controller for interval type-2 fuzzy systems. For the stabilization process, an event-triggered controller that does not share the same membership functions as the system is designed by using affine transformation parameters. Following that, by using an asymmetric Lyapunov–Krasovskii functional and some advanced integral inequalities to establish the sufficient conditions for the existence of the proposed controller. Furthermore, an asymptotic stabilization result is presented and discussed in a comparative analysis as a special case. Due to the asymmetric Lyapunov–Krasovskii functional structure, the transmission delay interval is large compared with some recent studies. Finally, two simulation examples are carried out and the efficiency of designed controller is verified.

A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties

This paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one initial fuzzy rule. The rules are then continuously updated and pruned in an online fashion to achieve the desired set point. For the phase of learning parameters, all adjustable parameters of the fuzzy system are tuned by using a sliding surface method, which is based on the gradient descent algorithm. This method is used to minimize the difference between the expected and actual signals. Our proposed control method is model-free and does not require prior knowledge of the plant’s dynamics or constraints. Instead, data-driven control utilizes artificial intelligence-based techniques, such as fuzzy logic systems, to learn the dynamics of the system and adapt to changes in the system, and account for complex interactions between different components. A robustness term is incorporated into the control effort to deal with external disturbances in the system. The proposed technique is applied to regulate the dynamics of a mobile robot in the presence of multiple external disturbances, demonstrating the robustness of the proposed control systems. A rigorous comparative study with respect to three different controllers is performed, where the outcomes illustrate the superiority of the proposed learning method as evidenced by lower RMSE values and fewer fuzzy parameters. Lastly, stability analysis of the proposed control method is conducted using the Lyapunov stability theory.

Non-fragile sliding mode observer based fault estimation for interval type-2 fuzzy singular fractional order systems

In this paper, an interval type-2 fuzzy model is presented to describe the nonlinear singular fractional order systems (SFOSs) with . The problem of actuator and sensor fault estimation is addressed by designing a non-fragile sliding mode observer (SMO). Firstly, a new criterion is provided to analyse the admissibility of SFOSs, which does not need to divide the fractional order interval into interval and interval to study them separately. Then, by expanding the dimension of the SFOS, the non-fragile SMO and the sliding surface are constructed and the accurate estimations of faults and the SFOS state simultaneously are achieved. A new reaching law is designed, which can divide the process of the system moving to the sliding surface into three stages. Further the control law is constructed to guarantee the observation error reaches the sliding surface in finite time. Finally, three examples show the effectiveness of the proposed scheme.

Hybrid simplification algorithm for unorganized point cloud based on two-level fuzzy decision making

Traditional point cloud simplification algorithms have specific application scenarios. If these well-known methods can be accurately applied to different feature regions of a point cloud, high-quality point cloud feature preservation can be realized. In this paper, a hybrid point cloud simplification method, based on two-level fuzzy decision making, is proposed. Specifically, the number of peak points and bin intervals of each dimension density histogram of the point cloud are counted, and the cluster number and initial centers of fuzzy c-means (FCM) clustering method are determined, hence the autonomous FCM clustering method of point clouds is realized. Based on the expert linguistic inference rules of type-1 fuzzy system, its output is used to accurately identify clusters as flat, transitional and drastic type. According to the attributes of the clusters, predefined thresholds or algorithms are applied in each one, achieving high quality point cloud simplification. The experimental results show that the algorithm can effectively determine the number of clusters, improve the speed of FCM clustering algorithm and achieve feature preserving point cloud simplification, after removing 50–90 % of the points.

Type-1 and Interval Type-2 Fuzzy Systems [AI- eXplained

Fuzzy sets are suitable for modeling human linguistic uncertainties, and the resulting fuzzy systems can then be used to perform inferences based on a linguistic rulebase. They have been successfully used in numerous applications, particularly controls. This short paper introduces the basics of type-1 and interval type-2 fuzzy sets and systems, particularly for newcomers. The online full paper at IEEE Xplore provides detailed examples for better understanding, and interactive components for a hands-on exploration.

Combination of Hedge Algebra and Type-2 Fuzzy System for Electrocardiogram Signal Recognition and Classification

The paper presents a new application of Hedge Algebra in Type 2 (HAT2) Fuzzy System (FS) for electrocardiographical signal recognition and classification. The HAT2 integrates the capability of fuzzy logic in modeling uncertainties of data and the linguistic variables to describe and manipulate the knowledge in a way closer to the way humans describe facts and rules. An adaptive learning algorithm will be also proposed to train the system’s parameters for better fitting to the data samples. The proposed approach will be tested with the heart beat classification problem with different types of arrhythmias in the ECG signals taken from the Arrhythmia Databases of Massachusetts Institute of Technology and Boston's Beth Israel Hospital. The numerical results will be compared with other methods to show the high quality of proposed solution. The proposed solution is also shown being simple with low complexity of computation, which makes it suitable for use in IoT or portable devices for intelligent solutions used in modern healthcare systems.