Interval Type-2 Research Articles

Adaptive event-triggered secure control for IT2 fuzzy systems vulnerable to hybrid attacks and its application

This work addresses the adaptive event-triggered control issue that interval type-2 (IT2) fuzzy systems are vulnerable to hybrid attacks.They were initially developed for fuzzy systems by thoroughly addressing the characteristics of hybrid attacks, which include deception attacks and replay attacks. Our main goal is to mitigate the effects of hybrid attacks and protect communication resources by developing an event-triggered controller for IT2 fuzzy systems with adjustable thresholds. The proposed hybrid attacks are described by a stochastic random variable that satisfies the Bernoulli distribution. Based on Lyapunov-Krasvoskii stability theory, the resulting closed-loop IT2 fuzzy system clearly satisfies sufficient criteria to ensure mean square stability. Also, the weight matrices of the trigger condition and controller gain matrices are obtained using LMI techniques. Lastly, a numerical example is provided to illustrate the effectiveness and applicability of the proposed control strategy.

Stabilization of Interval Type-2 T–S Fuzzy Systems via Time-Dependent Memory Sampled-Data Control and Its Applications

Stabilization of Interval Type-2 T–S Fuzzy Systems via Time-Dependent Memory Sampled-Data Control and Its Applications

Practical consensus of T-S fuzzy positive multi-agent systems using linear programming

This paper presents the practical consensus of T-S fuzzy positive multi-agent systems with interval type-1 and type-2 fuzzy sets using observer-based control protocols. A fuzzy positive observer is first constructed for the systems. An observer-based fuzzy control protocol is designed, where an additional constant term is introduced. Some linear programming conditions are established to achieve the positivity of the original system and its observer. Then, the practical consensus of the original system is transformed into the stability of a dynamic system, where a set of new variables is defined by combining the constant term and the states of the agents. In the first step, the positivity of the new variables is guaranteed. In the second step, the stability of the dynamic system consisting new variables is addressed by using a fuzzy copositive Lyapunov function. The gain matrices of observer and control protocols are formulated based on a matrix decomposition approach. All positive and consensus conditions are described via linear programming. Finally, two examples are provided to verify the validity of the obtained results.

Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics

The Internet of Things (IoTs) revolutionizes the consumer electronics landscape by presenting a degree of personalization and interactivity that was previously unimaginable. Interconnected devices are now familiar with user characteristics, giving custom skills that improve the user's satisfaction. Still, IoT remains to transform the consumer electronics field; security in IoT becomes critical, and it is utilized by cyber attackers to pose risks to public safety, compromise data privacy, gain unauthorized access, and even disrupt operations. Robust security measures are crucial for maintaining trust in the proliferation and adoption of interconnected technologies, mitigating those risks, protecting sensitive data, and certifying the integrity of the IoT ecosystem. An intrusion detection system (IDS) is paramount in IoT security, as it dynamically monitors device behaviours and network traffic to detect and mitigate any possible cyber threats. Using machine learning (ML) methods and anomaly detection algorithms, IDS can rapidly identify abnormal activities, unauthorized access, or malicious behaviours within the IoT ecosystem, thus preserving the integrity of interconnected devices and networks, safeguarding sensitive data, and protecting against cyber-attacks. This work presents an Improved Crayfish Optimization Algorithm with Interval Type-2 Fuzzy Deep Learning (ICOA-IT2FDL) technique for Intrusion Detection on IoT infrastructure. The main intention of the ICOA-IT2FDL technique is to utilize a hyperparameter-tuned improved deep learning (DL) method for intrusion detection, thereby improving safety in the IoT infrastructure. BC technology can be used to accomplish security among consumer electronics. The ICOA-IT2FDL technique employs a linear scaling normalization (LSN) approach for data normalization. In addition, features are selected using an improved crayfish optimization algorithm (ICOA). This is followed by the ICOA-IT2FDL technique, which applies the interval type-2 fuzzy deep belief network (IT2-FDBN) model to identify intrusions. Finally, the bald eagle search (BES) model strategy improves the intrusion recognition rate. A series of investigations is accomplished to ensure the enhanced accomplishment of the ICOA-IT2FDL model. The experimentation results specified that the ICOA-IT2FDL model shows better recognition results compared to recent models.

Study on Non-iterative Algorithms for Center-of-Sets Type-Reduction of Interval Type-2 Takagi–Sugeno–Kang Fuzzy Logic Systems

Study on Non-iterative Algorithms for Center-of-Sets Type-Reduction of Interval Type-2 Takagi–Sugeno–Kang Fuzzy Logic Systems

A distributionally robust optimization approach of multi-park integrated energy systems considering shared energy storage and Uncertainty of Demand Response

To enhance the economic efficiency and renewable energy integration capacity of multi-park integrated energy systems (MPIES) and address the issue of insufficient consideration of demand response uncertainty in existing studies, this paper proposes a distributionally robust optimization approach for multi-park integrated energy systems, considering shared energy storage and the uncertainty of demand response. First, models of the shared energy system and demand response are established. Based on these models, a deterministic optimization scheduling model for MPIES is developed, aiming to minimize system costs while considering constraints such as grid power balance. To address uncertainty in demand response, this paper employs Interval Type-2 Fuzzy Theory to construct uncertainty sets and considers system reliability constraints. The original optimization problem is then transformed into an equivalent robust counterpart model, and the optimal distributionally robust solution is obtained through parameter domain decomposition methods. Finally, the proposed method is validated using the IEEE 33-bus system. The results show that considering shared energy storage and demand response individually can reduce total system costs by 4.86 % and 26.46 %, respectively. After accounting for the uncertainty in demand response, the total system cost increases only slightly by 4.36 %, but this improves the system's robustness.

Local Fuzzy-Basis-Dependent Stabilization of Interval Type-2 T–S Fuzzy Systems via an Improved Adaptive Memory-Based Event-Triggered Mechanism

Local Fuzzy-Basis-Dependent Stabilization of Interval Type-2 T–S Fuzzy Systems via an Improved Adaptive Memory-Based Event-Triggered Mechanism

Interval Type-2 Fuzzy Neural Network Based Cascade Predictive Control of Superheated Steam Temperature

Abstract The superheated steam temperature (SST) suffers from complex dynamic characteristics such as nonlinearity and large time delay. To deal with the negative effects, this paper proposes a cascade predictive control strategy (CPC-PI) based on interval type-2 fuzzy neural network (IT2FNN) to improve the performance of SST. First, this paper proposes the IT2FNN model for modeling the SST model. And then on the basis the local linearized IT2FNN model, the CPC-PI strategy is designed. In the following, fuzzy self-regulation of the weight factor in the CPCPI is carried out to further improve the dynamic response speed and stability of SST. Finally, the comparative simulations verify that the IT2FNN-based CPC-PI control strategy with self-regulated weight factor is superior to the PI-PI control strategy and the IT2FNN-based CPC-PI control strategy with fixed weight factor.

Cognitive Assessment of Scientific Creative Skill by Brain-Connectivity Analysis Using Graph Convolutional Interval Type-2 Fuzzy Network

Cognitive Assessment of Scientific Creative Skill by Brain-Connectivity Analysis Using Graph Convolutional Interval Type-2 Fuzzy Network

A Bayesian Network Inference Approach for Dynamic Risk Assessment Using Multisource-Based Information Fusion in an Interval Type-2 Fuzzy Set Environment

A Bayesian Network Inference Approach for Dynamic Risk Assessment Using Multisource-Based Information Fusion in an Interval Type-2 Fuzzy Set Environment

Interval Type-2 Fuzzy Sampled-Date Control for Nonlinear Systems with Packet Dropouts via a Switched System Approach

Interval Type-2 Fuzzy Sampled-Date Control for Nonlinear Systems with Packet Dropouts via a Switched System Approach

Integrated Fault-Tolerant Control Design With Sampled-Output Measurements for Interval Type-2 Takagi-Sugeno Fuzzy Systems.

This article is concerned with the integrated design of fault estimation (FE) and fault-tolerant control (FTC) for uncertain nonlinear systems suffering from actuator faults and external disturbance. The uncertain nonlinear systems are characterized as the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model, and IT2 membership functions are employed to effectively handle uncertainties. A fuzzy observer, utilizing only sampled-output measurements, is applied to simultaneously estimate actuator faults and system states. Based on the estimation, the fault-tolerant controller is designed to ensure the system stability under a predefined H∞ performance. The sampling behavior complicates the system dynamics and makes the integrated FTC design more challenging. To confront this issue, the discontinuous Lyapunov functional technique is exploited to enhance stability results by considering the sampling characteristic, upon which FE and FTC units are co-designed in the linear matrix inequality (LMI) framework. To further relax stability criteria, the analysis process incorporates the bound information of membership functions through the membership-function-dependent (MFD) method. Additionally, the relationship of mismatched premise variables resulting from the sampling scheme is also taken into account. Moreover, considering the imperfect premise matching (IPM) framework, the proposed fault-tolerant controller provides greater flexibility in selecting the shapes of membership functions and number of fuzzy rules that can vary from the counterpart of the fuzzy system. Finally, the efficacy of the proposed FTC technique is validated through a detailed numerical example.

Relaxed Stabilization of Interval Type-2 Fuzzy Semi-Markov Jump Systems With DoS Attacks: A Homogeneous Polynomial Approach

Relaxed Stabilization of Interval Type-2 Fuzzy Semi-Markov Jump Systems With DoS Attacks: A Homogeneous Polynomial Approach

Comparison of the Performance of Type-1 and Interval Type-2 Fuzzy PI Controllers for Liquid Level Control in a Coupled Tank System

In this study, liquid level control in a coupled tank system is realized with PI controllers whose parameters are adapted using fuzzy logic approximation methods. Type-1 fuzzy PI control, interval type-2 fuzzy PI control and classical PI+Feedforward control methods are applied to the tank system in real time for different reference liquid levels. The performances of the controllers are compared in terms of system response parameters such as rise time, settling time and overshoot percentage. In addition, the performance evaluation of the controllers over a certain period of time is also compared by calculating integral-based concepts that express error performance metrics such as integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE) and integral time absolute error (ITAE). The obtained results show that the type-2 fuzzy PI controller shows the best performance, followed by type-1 fuzzy PI control and classical PI+FF controller, respectively.

Uncertainty Minimization in the Human Visual Response Using an Interval Type-2 Fuzzy Set and Its Application to Rice Leaf Image Enhancement

Uncertainty Minimization in the Human Visual Response Using an Interval Type-2 Fuzzy Set and Its Application to Rice Leaf Image Enhancement

An Aperiodic-Sampling-Dependent Event-Triggered Control Strategy for Interval Type-2 Fuzzy Systems: New Communication Scheme and Discontinuous Functional.

This article studies the event-triggered control problem of interval type-2 (IT2) fuzzy systems. It proposes a new aperiodic-sampling-dependent event-triggered communication scheme, and introduces an improved sampling-dependent discontinuous functional. First, taking into account that the system is with aperiodic sampling, the weighting matrices used for judgement in the event-triggered scheme are designed to be related to each sampling interval or its upper and lower bounds, according to the sampling interval being predictable or not. This design makes the scheme more flexible, thereby improving control effectiveness. Second, a sampling-dependent discontinuous functional is introduced into event-triggered control, which contains matrix variables depending on the aperiodic sampling, and incorporates an improved discontinuous term and a vector related to event-triggered condition. Thus, the functional reduces the conservativeness of stability and stabilization criteria. Based on the above aperiodic-sampling-dependent strategy, the derived results achieve better event-triggered control effects compared with the recent literature. At the end, two examples are given to validate the theoretical effectiveness and advantages of the results under various parameters.

A Novel Interval Type-2 Fuzzy CPT-TODIM Method for Multi-criteria Group Decision Making and Its Application to Credit Risk Assessment in Supply Chain Finance

A Novel Interval Type-2 Fuzzy CPT-TODIM Method for Multi-criteria Group Decision Making and Its Application to Credit Risk Assessment in Supply Chain Finance

Robust anti-disturbance interval type-2 fuzzy control for interconnected nonlinear PDE systems via conjunct observer

This paper investigates a robust anti-disturbance interval type-2 (IT2) fuzzy control for interconnected nonlinear partial differential equation (PDE) systems subject to parameter uncertainties by the conjunct observer. First, an IT2 fuzzy model is adopted to remodel the target system. Second, a state observer with mismatched premise variables is constructed to solve the problem that the original system and the observer do not share a uniform premise variable. Moreover, a disturbance observer is designed to estimate the unknown external disturbances, which can be modeled by exogenous PDE systems. Then, utilizing the conjunct observation information, an anti-disturbance IT2 fuzzy control strategy is proposed to attenuate the effect of disturbances on the system performance while ensuring that the closed-loop system is stable. Finally, simulation results verify the effectiveness of the proposed method.

Interval Type-2 Fuzzy Logic Controller for Speed Control of DC Motor in an Intelligent Manner

The method of variable speed control use in direct current motors adopting a fuzzy logic controller is fast replacing as compare some of the earlier conventional methods. In Electrical drive system a dc motor is fed from a fully controlled single phase convertor but the control is fails in the uncertain condition. This type of problem makes a critical issue in power plant where lots of motor are working. So, there is need to control dc motor in such uncertain condition. This work gives a design based on simulation of intelligent control by Interval Type-2 Fuzzy Logic Controller (IT2FLC). The designed fuzzy controller is effective in control performance in abnormal or uncertain conditions. The simulation study has been carried out and result are compared with type-1 FLC. The Result clearly demonstrate the superiority of the IT2FLC and compare to other methods proposed system is capable to control dc motors and performed better in both steady-state and cyclic conditions with more reliability

A Novel Risk Assessment Approach for Open-Cast Coal Mines Using Hybrid MCDM Models with Interval Type-2 Fuzzy Sets: A Case Study in Türkiye

A Novel Risk Assessment Approach for Open-Cast Coal Mines Using Hybrid MCDM Models with Interval Type-2 Fuzzy Sets: A Case Study in Türkiye