A type-3 logic fuzzy system: Optimized by a correntropy based Kalman filter with adaptive fuzzy kernel size

Qasem, Sultan Noman; Ahmadian, Ali; Mohammadzadeh, Ardashir; Sakthivel, R.; Pahlevanzadeh, Bahareh

doi:10.1016/j.ins.2021.05.031

Cited by 83 publications

(38 citation statements)

References 40 publications

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“…The T3-FLS [23] is formulated to cope with uncertainties. In this paper, the new learning laws for optimizing the rules of T3-FLSs are utilized.…”

Section: Type-3 Flsmentioning

confidence: 99%

“…(2) Consider j-th fuzzy set (FS) for χ i as Ψj i . Then, the memberships for Ψj i at the levels q κ and qκ are written as [23]:…”

Section: Type-3 Flsmentioning

confidence: 99%

“…In [22], it was proven that high-order FLSs are more effective in control problems that include uncertainties and noisy faults. More recently, T3-FLSs were described [23]; these provide a better efficiency in various noisy applications. In addition, the reinforcement learning (RL) approach was utilized to compute output synchronization protocols to achieve cooperative output regulation in [24].…”

Section: Introduction 1literature Reviewmentioning

confidence: 99%

“…The optimization problem in the framework of LMIs is suggested and the solution to the optimization problem leads to the gain of a distributed controller. • Type-3 FLS of [23] is formulated to study the nonlinearities of heterogeneous MASs and design a novel hybrid controller. It should be noted that the type-3 FLS-based control of MASs with the aforementioned perturbations has not been studied.…”

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confidence: 99%

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A Hybrid Predictive Type-3 Fuzzy Control for Time-Delay Multi-Agent Systems

et al. 2021

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In this paper, the consensus problem is addressed for multi-agent systems. The dynamics of each agent contain unknown uncertain/nonlinear terms and unknown time delays. A type-3 fuzzy logic system is developed to tackle the effect of unknown dynamics and design a hybrid controller. The policy scheme involves two control signals for the stabilization of the approximation and consensus error of each agent dynamic. To this end, based on the concept of the model predictive control approach, the constrained control laws are designed and updated at each time step. The simulations results portray the error signals. Feasibility, appropriate convergence, and proper transient response are the main merits of the suggested method.

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“…The T3-FLS [23] is formulated to cope with uncertainties. In this paper, the new learning laws for optimizing the rules of T3-FLSs are utilized.…”

Section: Type-3 Flsmentioning

confidence: 99%

“…(2) Consider j-th fuzzy set (FS) for χ i as Ψj i . Then, the memberships for Ψj i at the levels q κ and qκ are written as [23]:…”

Section: Type-3 Flsmentioning

confidence: 99%

Section: Introduction 1literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

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A Hybrid Predictive Type-3 Fuzzy Control for Time-Delay Multi-Agent Systems

et al. 2021

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“…Moreover, to upgrade approximation performance, an interval type-3 (T3) FLS with an online learning scheme has been developed in [23]. Recently, a self-organizing interval T3-FLS has been suggested in [24] to promote the precisions of a learning algorithms in versus of non-Gaussian noises. In [25] a new FLS based predictive control system is introduced for networked systems.…”

Section: Introductionmentioning

confidence: 99%

A New Data-Driven Control System for MEMSs Gyroscopes: Dynamics Estimation by Type-3 Fuzzy Systems

Alattas

Mohammadzadeh²,

Mobayen

et al. 2021

Micromachines

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In this study, a novel data-driven control scheme is presented for MEMS gyroscopes (MEMS-Gs). The uncertainties are tackled by suggested type-3 fuzzy system with non-singleton fuzzification (NT3FS). Besides the dynamics uncertainties, the suggested NT3FS can also handle the input measurement errors. The rules of NT3FS are online tuned to better compensate the disturbances. By the input-output data set a data-driven scheme is designed, and a new LMI set is presented to ensure the stability. By several simulations and comparisons the superiority of the introduced control scheme is demonstrated.

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A methodology for building interval type‐3 fuzzy systems based on the principle of justifiable granularity

Castillo

Castro

Melín

2022

Int J of Intelligent Sys

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In this article a design methodology for Mamdani interval type-3 fuzzy systems with center-of-sets type reduction is outlined. The methodology utilizes statistical measures, fuzzy c-means clustering and granular computing, to establish the justifiable footprint of uncertainty (JFOU) of the fuzzy granules, as explainable semantic abstractions that form the fuzzy model. The design methodology is presented in three general steps, first we use the principle of justifiable granularity to build a diagram of the justifiable information granule that contains a data structure with the descriptive measures of the experimental evidence of the data set. These measures are obtained from the

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A type-3 logic fuzzy system: Optimized by a correntropy based Kalman filter with adaptive fuzzy kernel size

Cited by 83 publications

References 40 publications

A Hybrid Predictive Type-3 Fuzzy Control for Time-Delay Multi-Agent Systems

A Hybrid Predictive Type-3 Fuzzy Control for Time-Delay Multi-Agent Systems

A New Data-Driven Control System for MEMSs Gyroscopes: Dynamics Estimation by Type-3 Fuzzy Systems

A methodology for building interval type‐3 fuzzy systems based on the principle of justifiable granularity

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