| 1. |
- Aslam, Muhammad Shamrooz, et al.
(author)
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Observer–Based Control for a New Stochastic Maximum Power Point tracking for Photovoltaic Systems With Networked Control System
- 2023
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In: IEEE transactions on fuzzy systems. - Piscataway, NJ : IEEE. - 1063-6706 .- 1941-0034. ; 31:6, s. 1870-1884
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Journal article (peer-reviewed)abstract
- This study discusses the new stochastic maximum power point tracking (MPPT) control approach towards the photovoltaic cells (PCs). PC generator is isolated from the grid, resulting in a direct current (DC) microgrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposed a Networked Control Systems (NCSs) beneath an event-triggered approach basically in the fuzzy system. In this scenario, we look at how random, variable loads impact the PC generator's stability and efficiency. The basic premise of this article is to load changes and the value matching to a Markov chain. PC generators are complicated nonlinear systems that pose a modeling problem. Transforming this nonlinear PC generator model into the Takagi–Sugeno (T–S) fuzzy model is another option. Takagi–Sugeno (T–S) fuzzy model is presented in a unified framework, for which 1) the fuzzy observer–based on this premise variables can be used for approximately in the infinite states to the present system, 2) the fuzzy observer–based controller can be created using this same premises be the observer, and 3) to reduce the impact of transmission burden, an event-triggered method can be investigated. Simulating in the PC generator model for the realtime climate data obtained in China demonstrates the importance of our method. In addition, by using a new Lyapunov–Krasovskii functional (LKF) for combining to the allowed weighting matrices incorporating mode-dependent integral terms, the developed model can be stochastically stable and achieves the required performances. Based on the T-P transformation, a new depiction of the nonlinear system is derived in two separate steps in which an adequate controller input is guaranteed in the first step and an adequate vertex polytope is ensured in the second step. To present the potential of our proposed method, we simulate it for PC generators. © 2022 IEEE.
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| 2. |
- Cao, Bin, et al.
(author)
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Multiobjective Evolution of the Explainable Fuzzy Rough Neural Network With Gene Expression Programming
- 2022
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In: IEEE transactions on fuzzy systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1063-6706 .- 1941-0034. ; 30:10, s. 4190-4200
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Journal article (peer-reviewed)abstract
- The fuzzy logic-based neural network usually forms fuzzy rules via multiplying the input membership degrees, which lacks expressiveness and flexibility. In this article, a novel neural network model is designed by integrating the gene expression programming into the interval type-2 fuzzy rough neural network, aiming to generate fuzzy rules with more expressiveness utilizing various logical operators. The network training is regarded as a multiobjective optimization problem through simultaneously considering network precision, explainability, and generalization. Specifically, the network complexity can be minimized to generate concise and few fuzzy rules for improving the network explainability. Inspired by the extreme learning machine and the broad learning system, an enhanced distributed parallel multiobjective evolutionary algorithm is proposed. This evolutionary algorithm can flexibly explore the forms of fuzzy rules, and the weight refinement of the final layer can significantly improve precision and convergence by solving the pseudoinverse. Experimental results show that the proposed multiobjective evolutionary network framework is superior in both effectiveness and explainability.
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| 3. |
- Ding, Yijie, et al.
(author)
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Fuzzy Neural Tangent Kernel Model for Identifying DNA N4-methylcytosine Sites
- 2024
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In: IEEE transactions on fuzzy systems. - Piscataway, NJ : IEEE. - 1063-6706 .- 1941-0034.
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Journal article (peer-reviewed)abstract
- DNA N4-methylcytosine (4mC) site identification is a crucial field in bioinformatics, where machine learning methods have been effectively utilized. Due to the presence of noise, the existing deep learning methods for detecting 4mC have consistently low recognition rates in positive samples. With fuzzy rules and membership functions, fuzzy systems can achieve good results in processing noisy signals. In contrast to traditional fuzzy systems that lack deep feature representation and sample measurement, we introduce novel techniques to enhance generalization and feature representation. By incorporating the neural tangent kernel (NTK) and kernel learning algorithm into the fuzzy system, we propose the fuzzy neural tangent kernel (FNTK) model and the radius-based FNTK (R-FNTK) model to predict DNA 4mC sites. To achieve better generalization performance than traditional kernel functions, we first train the NTK for feature representation learning and sample measurement. Based on the membership function and NTK matrix, different fuzzy kernel matrices are constructed for each fuzzy subset of the fuzzy system. Finally, we utilize two types of iterative kernel optimization algorithms to effectively fuse multiple NTK-based fuzzy kernels and obtain the final prediction model. Rigorous testing using 6 benchmark datasets demonstrates the superiority of our approach, yielding significant improvements in the experiment's performance. © IEEE
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| 4. |
- Emami, Reza, et al.
(author)
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Development of a systematic methodology of fuzzy logic modeling
- 1998
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In: IEEE transactions on fuzzy systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1063-6706 .- 1941-0034. ; 6:3, s. 346-361
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Journal article (peer-reviewed)abstract
- This paper proposs a systematic methodology of fuzzy logic modeling as a generic tool for modeling of complex systems. The methodology conveys three distinct features: 1) a unified parameterized reasoning formulation; 2) an improved fuzzy clustering algorithm; and 3) an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces four parameters whose variation provides a continuous range of inference operation. As a result, we are no longer restricted to standard extremes in any step of reasoning. Unlike traditional approach of selecting the inference mechanism a priori, the fuzzy model itself can then adjust the reasoning process by optimizing the inference parameters based on input-output data. The fuzzy rules are generated through fuzzy c-means (FCM) clustering algorithm. Major bottle-necks of the algorithm are addressed and analytical solutions are suggested. Furthermore, we also address the classification process in fuzzy modelng to extend the derived fuzzy partition to the entire output space. This issue remains unattained in the current literature. In order to select suitable input variables among a finite number of candidates (unlike traditional approaches) we suggest a new strategy through which dominant input parameters are assigned in one step and no iteration process is required. Furthermore, a clustering technique called fuzzy line clustering is introduced to assign the input membership functions. In order to evaluate the proposed methodology, two examples - a nonlinear function and a gas furnace dynamic procedure - are investigated in detail. The significant improvement of the model is concluded compared to other fuzzy modeling approaches. © 1998 IEEE.
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| 5. |
- Herman, Pawel, et al.
(author)
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Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain-Computer Interface Classification of Motor Imagery Induced EEG Patterns
- 2017
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In: IEEE transactions on fuzzy systems. - : IEEE Press. - 1063-6706 .- 1941-0034. ; 25:1, s. 29-42
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Journal article (peer-reviewed)abstract
- One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to the classification task within brain-computer interface (BCI) systems. Recently emerged type-2 fuzzy logic (T2FL) methodology has shown a remarkable potential in dealing with uncertain information given limited insight into the nature of the data-generating mechanism. The objective of this work is, thus, to examine the applicability of the T2FL approach to the problem of EEG pattern recognition. In particular, the focus is two-fold: 1) the design methodology for the interval T2FL system (IT2FLS) that can robustly deal with inter-session as well as within-session manifestations of nonstationary spectral EEG correlates of motor imagery, and 2) the comprehensive examination of the proposed fuzzy classifier in both off-line and on-line EEG classification case studies. The on-line evaluation of the IT2FLS-controlled real-time neurofeedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysis accounting for other popular BCI classifiers such as linear discriminant analysis, kernel Fisher discriminant, and support vector machines as well as a conventional type-1 FLS, simulated off-line on the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification.
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| 6. |
- Kadmiry, Bourhane, et al.
(author)
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A fuzzy gain-scheduler for the attitude control of an unmanned helicopter
- 2004
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In: IEEE transactions on fuzzy systems. - : IEEE Computer Society. - 1063-6706 .- 1941-0034. ; 12:4, s. 502-515
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Journal article (peer-reviewed)abstract
- In this paper, we address the design of an attitude controller that achieves stable, and robust aggressive maneuverability for an unmanned helicopter. The controller proposed is in the form of a fuzzy gain-scheduler, and is used for stable and robust altitude, roll, pitch, and yaw control. The controller is obtained from a realistic nonlinear multiple-input-multiple-output model of a real unmanned helicopter platform, the APID-MK3. The results of this work are illustrated by extensive simulation, showing that the objective of aggressive, and robust maneuverability has been achieved.
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| 7. |
- Liao, Qianfang, 1983-, et al.
(author)
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Interaction Measures for Control Configuration Selection Based on Interval Type-2 Takagi-Sugeno Fuzzy Model
- 2018
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In: IEEE transactions on fuzzy systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1063-6706 .- 1941-0034. ; 26:5, s. 2510-2523
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Journal article (peer-reviewed)abstract
- Interaction measure determines decentralized and parse control configurations for a multivariable process control. This paper investigates interval type-2 Takagi–Sugeno fuzzy (IT2TSF) model based interactionmeasures using two different criteria, one is controllability and observability gramians, the other is relative normalized gain array (RNGA). The main contributions are: first, a data-driven IT2TSF modeling method is introduced; econd, explicit formulas to execute the two measures based on IT2TSF models are given; third, two interaction indexes are defined from RNGA to select sparse control configuration; fourth, the calculations to derive sensitivities of the two measures with respect to parametric variations in the IT2TSF models are developed; and fifth, the discussion to compare the two measures is presented. Three multivariable processes are used as examples to show that the results calculated from IT2TSF models are more accurate than that from their type-1 counterparts, and compared to gramian-basedmeasure, RNGA selectsmore reasonable control configurations and is more robust to the parametric uncertainties.
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| 8. |
- Narukawa, Yasuo, et al.
(author)
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Scores for hesitant fuzzy sets : aggregation functions and generalized integrals
- 2023
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In: IEEE transactions on fuzzy systems. - : IEEE. - 1063-6706 .- 1941-0034. ; 31:7, s. 2425-2434
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Journal article (peer-reviewed)abstract
- There are several extensions of fuzzy sets. Hesitant fuzzy sets are one of them. They are defined in terms of a set of membership degrees. For a typical hesitant fuzzy set, this set of membership degrees has a finite number of values. One of the motivations to introduce score functions was to rank alternatives. In this case, as the membership degrees are a set, the comparison of membership values does not lead, in general, to a total order. Score functions can be seen as functions that transform the set of membership degrees into a single membership value. In this way, we can construct the total order. In this article, we propose a general framework to define score functions for hesitant fuzzy sets based on fuzzy integrals. This framework permits to see most relevant indices as particular cases. Moreover, previous approaches focused on typical hesitant fuzzy sets. Our approach is more general in the sense that we can process both typical hesitant fuzzy sets and the nontypical ones (with membership values that are not finite). We also frame the problem into a more general setting. That is, the problem of hesitant fuzzy set transformation. Score functions can be seen as functions that transform a hesitant fuzzy set into a standard fuzzy set. Similarly, we can consider its transformation to interval-valued fuzzy sets and type-2 fuzzy sets. Aggregation functions can also be used for the same purpose.
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| 9. |
- Nie, Linlin, et al.
(author)
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Improved Nonlinear Extended Observer Based Adaptive Fuzzy Output Feedback Control for a Class of Uncertain Nonlinear Systems With Unknown Input Hysteresis
- 2023
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In: IEEE Transactions on Fuzzy Systems. - 1941-0034 .- 1063-6706. ; 31:10, s. 3679-3689
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Journal article (peer-reviewed)abstract
- This study focuses on the problem of adaptive fuzzy dynamic surface output feedback control for a class of uncertain nonlinear systems subjected to unknown input hysteresis. A Prandtl-Ishlinskii (PI) model is applied to the uncertain nonlinear system for describing the unknown input hysteresis, making the controller design feasible. In addition, a nonlinear extended state observer (NESO) is designed for simultaneously estimating the unmeasurable states and generalized disturbances, including the nonlinear hysteresis term of the PI model and external disturbances. In addition, a novel nonlinear function is designed to replace fal(·) function of the general NESO to address a modification that increases the convergence speed. Considering the incorporation of the improved nonlinear extended state observer (INESO), an adaptive output feedback control scheme is proposed based on fuzzy logic system and dynamic surface techniques. A command filter is employed to avoid the 'explosion of complexity' problem inherent in the backstepping technique, while compensating the filtering error caused by adopting the filter. The Lyapunov approach is used to demonstrate the stability of the entire closed-loop system. Experiments regarding a piezoelectric micropositioning stage are conducted, the results of which illustrate that the proposed adaptive fuzzy output feedback control method can guarantee a satisfactory tracking performance.
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| 10. |
- Qu, Zhiguo, et al.
(author)
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Quantum Fuzzy Federated Learning for Privacy Protection in Intelligent Information Processing
- 2024
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In: IEEE transactions on fuzzy systems. - Piscataway, NJ : IEEE. - 1063-6706 .- 1941-0034.
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Journal article (peer-reviewed)abstract
- With the advent of the intelligent information processing era, more and more private sensitive data are being collected and analyzed for intelligent decision making tasks. Such information processing also brings many challenges with existing privacy protection algorithms. On the one hand, the algorithms based on data encryption compromise the integrity of the original data or incur high computational and communication costs to some extent. On the other hand, algorithms based on distributed learning require frequent sharing of parameters between different computing nodes, which poses risks of leaking local model information and reducing global learning efficiency. To mitigate the impact of these issues, a Quantum Fuzzy Federated Learning (QFFL) algorithm is proposed. In the QFFL algorithm, a Quantum Fuzzy Neural Network (QFNN) is designed at the local computing nodes, which enhances data generalization while preserving data integrity. In global model, QFFL makes predictions through the Quantum Federated Inference (QFI). QFI leads to a general framework for quantum federated learning on non-IID data with oneshot communication complexity, achieving privacy protection of local data and accelerating the global learning efficiency of the algorithm. The experiments are conducted on the COVID19 and MNIST datasets, and the results indicate that QFFL demonstrates superior performance compared to the baselines, manifesting in faster training efficiency, higher accuracy, and enhanced security. In addition, based on the fidelity experiments and related analysis under four common quantum noise channels, the results demonstrated that it has good robustness against quantum noises, proving its applicability and practicality. Our code is available at https://github.com/LASTsue/QFFL. © IEEE
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