Experimental Evaluation on Defuzzification of TSK-type-based Interval Type-2 Fuzzy Inference Systems

Zhan, Tao; Li, Wentao; Fan, Bingjiao; Liu, Shuai

doi:10.1007/s12555-021-0370-z

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…It is well known that stabilization is a prerequisite for a system to be able to operate properly, so the method of achieving system stabilization by designing suitable external control has received wide attention. Currently, many experts have proposed many effective control methods, such as event-triggered control, 11 tracking control, 12 feedback control, 13 impulse control, 14 fuzzy control 15 and hybrid control, 16,17 and so forth, which have their own advantages and should be chosen according to the specific situation in dealing with practical problems. This paper is a study of the stabilization problems of networked systems, and it is well-known that when signals are transmitted between physical devices through network media, it is easy to cause waste of communication resources and lead to communication channel congestion.…”

Section: Introductionmentioning

confidence: 99%

Resilient adaptive event‐triggered synchronization control of piecewise‐homogeneous Markov jump delayed neural networks under aperiodic DoS attacks

Zhao,

Zhang,

Xiong

et al. 2023

Intl J Robust & Nonlinear

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SummaryUnder the aperiodic Denial‐of‐service (DoS) attacks, this paper studies the resilient adaptive event‐triggered synchronization control problem for a class of Piecewise‐Homogeneous Uncertain Markov Jump Neural Networks (PHUMJNNs) with time‐varying delays. First of all, a new way of carving DoS attacks is given from the defenders' perspective, that is, aperiodic DoS attacks based on fixed detection periods. Then, under such attacks, a new Resilient Adaptive Event‐triggered Communication (RAETC) is designed between sensor and controller, which has a threshold function based on the net change rate and is real‐time updated depending on the present sampling state. Next, a single functional is used in the construction of the Lyapunov‐Krasovskii functional, while a new looped functional is introduced that makes full use of the state information of the current instant , the trigger instant and the next trigger instant . Based on the constructed single functional, under the framework of the input delay method and the linear matrix inequality technique, the exponential mean square stabilization criterion of the error system is obtained, which makes the master system and the slave system synchronized. In the end, three simulation examples are used to illustrate the validity of the obtained results.

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Section: Introductionmentioning

confidence: 99%

Resilient adaptive event‐triggered synchronization control of piecewise‐homogeneous Markov jump delayed neural networks under aperiodic DoS attacks

Zhao,

Zhang,

Xiong

et al. 2023

Intl J Robust & Nonlinear

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T2RFIS: type-2 regression-based fuzzy inference system

Wiktorowicz

2023

Neural Comput & Applic

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This article discusses a novel type-2 fuzzy inference system with multiple variables in which no fuzzy rules are explicitly defined. By using a rule-free system, we avoid the serious disadvantage of rule-based systems, which are burdened with the curse of dimensionality. In the proposed system, Gaussian membership functions are used for its inputs, and linearly parameterized system functions are used to obtain its output. To obtain the system parameters, a genetic algorithm with multi-objective function is applied. In the presented method, the genetic algorithm is combined with a feature selection method and a regularized ridge regression. The objective functions consist of a pair in which one function is defined as the number of active features and the other as the validation error for regression models or the accuracy for classification models. In this way, the models are selected from the Pareto front considering some compromise between their quality and simplification. Compared to the author’s previous work on the regression-based fuzzy inference system, a new inference scheme with type-2 fuzzy sets has been proposed, and the quality has been improved compared to the system based on type-1 fuzzy sets. Four experiments involving the approximation of a function, the prediction of fuel consumption, the classification of breast tissue, and the prediction of concrete compressive strength confirmed the efficacy of the presented method.

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Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson’s Disease Using Biomedical Voice Measures

Nilashi,

Abumalloh,

Ahmadi

et al. 2024

Int. J. Fuzzy Syst.

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Experimental Evaluation on Defuzzification of TSK-type-based Interval Type-2 Fuzzy Inference Systems

Cited by 4 publications

References 30 publications

Resilient adaptive event‐triggered synchronization control of piecewise‐homogeneous Markov jump delayed neural networks under aperiodic DoS attacks

Resilient adaptive event‐triggered synchronization control of piecewise‐homogeneous Markov jump delayed neural networks under aperiodic DoS attacks

T2RFIS: type-2 regression-based fuzzy inference system

Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson’s Disease Using Biomedical Voice Measures

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