Wavelet Interval Type-2 Takagi-Kang-Sugeno Hybrid Controller for Time-Series Prediction and Chaotic Synchronization

Pham, Duc Hung; Lin, Chih‐Min; Giap, Van Nam; Lin, Chih-Min; Cho, Hsing-Yueh

doi:10.1109/access.2022.3210260

Cited by 9 publications

(5 citation statements)

References 36 publications

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“…Giap et al (Nguyen and Huang 2021) presented a 3D Lorentz chaos system for secure transmission, whereas Lin et al (Lin et al 2021a) proposed a 4D memristive chaos synchronization for image encryption. Recent research Pham et al (Pham et al 2022) produced a wavelet interval type-2 Takagi-Sugeno-Kang hybrid controller for time-series prediction and chaotic systems. Hosny and his colleagues created a 4D hyperchaotic Chen system for encrypting color images (Hosny et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…There are two types of fuzzy systems: TSK fuzzy systems (FS) and Mamdani-Larsen fuzzy systems (Pham et al, 2022), (Tan and Chua 2007). Zhou et al introduced first-order TSK fuzzy system (Zhou et al 2022) for nonlinear systems problems.…”

Section: Introductionmentioning

confidence: 99%

“…Lucas et al (Lucas et al 2004) designed the Brain-Imitated Neural Network (BINN) to address these issues. When the BINN is combined with another system, such as fuzzy system (Huynh et al 2022), TSK fuzzy system, and type-2 fuzzy system (Le et al 2018), to improve its performance. BINN is utilized for chaotic 3D synchronization and secure image communication (Lin et al 2021b).…”

Section: Introductionmentioning

confidence: 99%

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Secure Transmission of Medical Image Using a Wavelet Interval Type-2 TSK Fuzzy Brain-Imitated Neural Network

Pham,

Huynh,

Lin

et al. 2024

Preprint

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The purpose of this research is to develop a new design of a wavelet interval type-2 takagi-sugeno-kang fuzzy brain-imitated neural network (WIT2TFBINN), which is a combination of the mathematical models of a Takagi-Sugeno-Kang (TSK) fuzzy system based on wavelet interval type-2 function (WIT2) and a wavelet interval type-2 fuzzy brain imitated neural network (FBINN). The proposed WIT2TFBINN is used for synchronization control of a 4D Lorentz chaotic system and has the benefits of wavelet interval type-2 membership function, TSK fuzzy inference system, decision making, and emotional activity. To provide fast training, the proposed method's parameter update laws are derived using the gradient descent method. The proposed WIT2TFBINN synchronization technique is then applied to the transmission of medical images in a secure manner. As a cipher image, a medical image is encrypted into a chaotic trajectory. After transmission, the image can be decrypted using chaotic trajectory synchronization on the received signal. By comparing the root mean square error and statistical test results of the proposed method with recent methods, the superiority of the proposed method is demonstrated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Secure Transmission of Medical Image Using a Wavelet Interval Type-2 TSK Fuzzy Brain-Imitated Neural Network

Pham,

Huynh,

Lin

et al. 2024

Preprint

Self Cite

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“…However, many external and internal disturbances are unavoidable and occur in many practical applications. Type-2 fuzzy logic systems, an extension of type-1 fuzzy logic systems used for FNNs and fuzzy BELCs [12], [13], have been proposed to mitigate the effects of such uncertainties. Type-2 fuzzy elliptic functions are more general, and can control the fuzzy footprint, manage and represent uncertainties, and make decisions with incomplete or ambiguous data [14].…”

Section: Introductionmentioning

confidence: 99%

“…The components of the BINN are: Input, Learning, Output, Emotion, Emotion Output, and Controller Output. By combining with other systems such as CMAC [13], [25], function-link [26], TSK fuzzy system [13], [27], wavelet type-2 fuzzy system [13], and type-2 fuzzy system [28], the performance of BINN can be improved.…”

Section: Introductionmentioning

confidence: 99%

Design of Missile Guidance Law Using Takagi-Sugeno-Kang (TSK) Elliptic Type-2 Fuzzy Brain Imitated Neural Networks

et al. 2023

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The exploiting of missile guidance law is one of the most important issue of defense systems. However, it is a complex nonlinear guidance control problem. In recent years, several intelligent algorithms have been employed for the missile guidance law design. This paper aims to provide a more effective intelligent neural network, and then apply it to missile guidance control. An elliptic type-2 function (ET2F) combined with a Takagi-Sugeno-Kang (TSK) fuzzy system (FS) based on a wavelet function and a brain imitated neural network (BINN) is proposed to produce a new Takagi-Sugeno-Kang elliptic type-2 fuzzy brain imitated neural network (TSKET2FBINN). The proposed TSKET2FBINN is then incorporated into the missile guidance law. The proposed missile guidance control system includes the TSKET2FBINN controller and a robust compensation controller. A Lyapunov function is used to generate parameter adjustment adaptive laws so as to guarantee system stability with fast tracking performance of missile guidance control system. By presenting three missile control scenarios, the effectiveness of the proposed method is then demonstrated. The miss-distance (MD) of the proposed method is calculated and compared to other recent guidance methods to demonstrate its superior performance.INDEX TERMS Elliptic type-2 function, TSK fuzzy system, brain imitated neural network, missile guidance law, command to line-of-sight.

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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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Wavelet Interval Type-2 Takagi-Kang-Sugeno Hybrid Controller for Time-Series Prediction and Chaotic Synchronization

Cited by 9 publications

References 36 publications

Secure Transmission of Medical Image Using a Wavelet Interval Type-2 TSK Fuzzy Brain-Imitated Neural Network

Secure Transmission of Medical Image Using a Wavelet Interval Type-2 TSK Fuzzy Brain-Imitated Neural Network

Design of Missile Guidance Law Using Takagi-Sugeno-Kang (TSK) Elliptic Type-2 Fuzzy Brain Imitated Neural Networks

T2RFIS: type-2 regression-based fuzzy inference system

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