Fuzzy Sets and Systems最新文献

英文中文

Practically predefined-time adaptive fuzzy control for stochastic nonlinear systems with full state constraints and dead zones 具有全状态约束和死区随机非线性系统的实际预定义时间自适应模糊控制

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems

Pub Date : 2026-05-15 Epub Date: 2026-01-15 DOI: 10.1016/j.fss.2026.109779

Mengqing Cheng , Shuo Shan , Junsheng Zhao , Shixiong Fang , Haikun Wei , Kanjian Zhang

This paper investigates a dynamic event-triggered practically predefined-time control (PPTC) scheme for stochastic nonlinear systems (SNSs) under state constraints and input dead zones. A nonlinear state-dependent function (NSDF) is introduced to enforce state constraints. To address input dead-zone nonlinearities and mitigate the complexity growth inherent in backstepping, novel predefined-time compensating filters (PTCFs) and predefined-time filters (PTFs) are designed. These filters guarantee the convergence of filter states within a predefined time and effectively suppress the influence of dead zones. Building on this, a semiglobal predefined-time adaptive fuzzy tracking control algorithm is developed, where a fuzzy logic system (FLS) approximates unknown nonlinear dynamics. Furthermore, a dynamic event-triggered mechanism (DETM) is incorporated into the framework to reduce communication load. The present control scheme ensures tracking error convergence within a predefined time and uniform boundedness of all closed-loop signals in the pth moment. Finally, a simulation example is conducted to demonstrate the effectiveness of the proposed strategy.

研究了随机非线性系统在状态约束和输入死区条件下的动态事件触发实际预定义时间控制（PPTC）方案。引入非线性状态相关函数（NSDF）来加强状态约束。为了解决输入死区非线性问题并减轻反推过程中固有的复杂性增长，设计了新型的预定义时间补偿滤波器（ptcf）和预定义时间滤波器（PTFs）。这些滤波器保证了滤波器状态在预定义时间内的收敛性，并有效地抑制了死区的影响。在此基础上，提出了一种半全局预定义时间自适应模糊跟踪控制算法，其中模糊逻辑系统（FLS）逼近未知非线性动力学。此外，该框架还引入了动态事件触发机制（DETM），以减少通信负荷。该控制方案保证了跟踪误差在预定时间内收敛，并保证了所有闭环信号在第pth时刻的均匀有界性。最后，通过仿真实例验证了所提策略的有效性。

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引用次数: 0

A long-term prediction model with Gaussian linear fuzzy granules based on convolutional neural networks and long short-term memory 基于卷积神经网络和长短期记忆的高斯线性模糊颗粒长期预测模型

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems

Pub Date : 2026-05-01 Epub Date: 2026-01-06 DOI: 10.1016/j.fss.2026.109765

Xueling Ma , Chenglong Zhu , Weiping Ding , Pierpaolo D’ Urso , Jianming Zhan

Long-term time series forecasting constitutes a significant area of research within data mining, pattern analysis, and recognition. Although the Gaussian linear fuzzy information granule (GLFIG) has garnered increasing attention as an efficient approach for long-term time series forecasting, it continues to face substantial challenges, including the quantification of trend extraction objectives, the non-metric nature of granular distance formulations, and the effective mining of granular structural information. To address these challenges, this study designs a long-term forecasting model based on GLFIGs, incorporating optimized trend information and enhanced periodic patterns. First, the model improves the extraction of trend information through an l₁-trend filter, which provides a principled basis for parameter selection. Second, periodic structures within the granular time series are refined to mitigate structural distortions caused by real-world data complexity, and a metric-compliant distance formula for GLFIGs of unequal length is introduced for the first time. Finally, a CNN-LSTM architecture augmented with periodic information is employed for long-term forecasting, leveraging long short-term memory (LSTM) to complement the limited temporal sensitivity of convolutional neural networks (CNNs). Experiments conducted on ten publicly available time series datasets demonstrate that the proposed model achieves satisfactory predictive accuracy in long-term univariate time series forecasting.

长期时间序列预测是数据挖掘、模式分析和识别中的一个重要研究领域。尽管高斯线性模糊信息颗粒（GLFIG）作为一种有效的长期时间序列预测方法受到越来越多的关注，但它仍然面临着实质性的挑战，包括趋势提取目标的量化、颗粒距离公式的非度量性质以及颗粒结构信息的有效挖掘。为了应对这些挑战，本研究设计了一个基于GLFIGs的长期预测模型，该模型结合了优化的趋势信息和增强的周期性模式。首先，该模型通过1- 1趋势过滤器改进了趋势信息的提取，为参数选择提供了原则依据。其次，细化粒度时间序列中的周期结构，以减轻现实世界数据复杂性造成的结构扭曲，并首次引入了不等长GLFIGs的度量兼容距离公式。最后，采用一种增强周期性信息的CNN-LSTM架构进行长期预测，利用长短期记忆（LSTM）来弥补卷积神经网络（cnn）有限的时间敏感性。在10个公开的时间序列数据集上进行的实验表明，该模型在长期单变量时间序列预测中取得了令人满意的预测精度。

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引用次数: 0

Forecasting time series collections via fuzzy clustering 通过模糊聚类预测时间序列集合

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems

Pub Date : 2026-05-01 Epub Date: 2025-12-29 DOI: 10.1016/j.fss.2025.109748

Ángel López Oriona, Ying Sun

The global approach has recently been shown to often outperform the local approach in time series forecasting. While the former fits a single model to all time series in a dataset, the latter fits a separate model to each series and therefore does not leverage potential similarities in the underlying structures across the time series collection. Empirical analyses have demonstrated that, in heterogeneous datasets, fitting per-cluster global models can further improve forecasting accuracy compared to the standard global approach. These methods obtain forecasts for a given time series using the global model associated with its corresponding cluster. In this paper, we show that combining the per-cluster approach with the fuzzy clustering paradigm can lead to even better predictive performance. Specifically, we propose a fuzzy clustering algorithm based on prediction accuracy of global models. The fuzzy partition is constructed by evaluating the prediction error of each series with respect to each global model. Forecasts for a given series are then obtained as a weighted average of the forecasts from all models, with weights determined by the corresponding membership degrees. The potential of the proposed approach is demonstrated using well-known time series datasets from several contexts. Improvements of up to 30% in forecasting error are achieved compared to the best of three strong benchmark methods.

最近的研究表明，在时间序列预测中，全局方法的表现往往优于局部方法。前者适用于数据集中所有时间序列的单一模型，而后者适用于每个序列的单独模型，因此不会利用跨时间序列集合的底层结构中的潜在相似性。实证分析表明，在异构数据集中，与标准全局方法相比，拟合每簇全局模型可以进一步提高预测精度。这些方法使用与其相应簇相关联的全局模型获得给定时间序列的预测。在本文中，我们证明了将每簇方法与模糊聚类范式相结合可以获得更好的预测性能。具体来说，我们提出了一种基于全局模型预测精度的模糊聚类算法。通过评价各序列相对于各全局模型的预测误差，构造模糊分区。然后将给定序列的预测作为所有模型预测的加权平均值，其权重由相应的隶属度确定。所提出的方法的潜力是用来自几个上下文的众所周知的时间序列数据集来证明的。与三种强基准方法中的最佳方法相比，预测误差提高了30%。

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引用次数: 0

On “another view on the non-additivity index of capacity” 关于“容量非可加性指标的另一种观点”

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems

Pub Date : 2026-05-01 Epub Date: 2026-01-04 DOI: 10.1016/j.fss.2026.109763

Jianzhang Wu

This comment concerns the paper “Another view on the non-additivity index of capacity” (Fuzzy Sets and Systems, Vol. 520, 2025, 109569). The classical non-additivity index (NI), defined through proper bipartitions, quantifies deviations from additivity. The generalized NI (GNI) proposed in the target paper extends this definition by including both the empty set and the coalition itself. We clarify that singleton cases are to be interpreted as importance rather than interaction, and show that the generalized extension adds little useful information and meanwhile misrepresents strict non-additivity. We further demonstrate that Properties 6, 7, and 8, together with the induced monotonicity constraints presented in the target paper as major contributions, are invalid and unreliable. Finally, we compare the properties of NI and GNI and emphasize that the classical NI uniquely satisfies key structural properties such as uniform range, maximality, and minimality.

本文对“关于容量非可加性指标的另一种看法”（模糊集与系统，第520卷，2025年，109569）进行了评论。经典的非可加性指数（NI），通过适当的二分法定义，量化偏离可加性。目标论文中提出的广义NI （GNI）扩展了这一定义，包括空集和联盟本身。我们澄清了单例情况应被解释为重要性而不是相互作用，并表明广义扩展增加了很少的有用信息，同时误解了严格的非可加性。我们进一步证明，性质6、7和8，以及目标论文中提出的诱导单调性约束是无效和不可靠的。最后，我们比较了NI和GNI的性质，并强调经典NI唯一满足均匀范围、极大性和极小性等关键结构性质。

引用次数: 0

Stability analysis of T-S fuzzy delayed impulsive systems with input saturation via an impulse-time-related function method 基于脉冲时间相关函数法的输入饱和T-S模糊延迟脉冲系统稳定性分析

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems

Pub Date : 2026-05-01 Epub Date: 2025-12-31 DOI: 10.1016/j.fss.2025.109750

Zhilong He , Chuandong Li , Cheng Hu , Zhiyong Yu , Haijun Jiang , Shiping Wen

This paper investigates the stability and stabilization of nonlinear time-delay systems via a novel Takagi-Sugeno (T-S) fuzzy hybrid impulsive controller that explicitly accounts for both input delay and saturation, enhancing its practical applicability. The main contributions are threefold. First, a new impulse-time-related Lyapunov function (ITRLF) is constructed, which synergistically integrates the impulsive Razumikhin technique with an improved convex hull representation to handle saturation nonlinearities effectively. Second, sufficient conditions in the form of linear matrix inequalities (LMIs) are established to ensure local exponential stability. A key advantage of these conditions is that they depend only on the bounds of impulse delays and intervals, eliminating the restrictive requirement of a specific relationship between them, thus reducing conservatism. Third, novel LMI-based optimization algorithms are proposed to maximize the estimation of the region of attraction (ROA), effectively trading off computational complexity for significantly reduced conservatism compared to conventional methods. The effectiveness and advantages of the proposed approach are validated through numerical simulations using the MATLAB LMI toolbox.

本文通过一种明确考虑输入延迟和饱和的新型Takagi-Sugeno （T-S）模糊混合脉冲控制器来研究非线性时滞系统的稳定性和镇定性，提高了它的实用性。主要贡献有三方面。首先，构造了一种新的脉冲时间相关Lyapunov函数（ITRLF），该函数将脉冲Razumikhin技术与改进的凸包表示协同结合，有效地处理了饱和非线性；其次，以线性矩阵不等式（lmi）的形式建立了保证局部指数稳定的充分条件。这些条件的一个关键优点是它们只依赖于脉冲延迟和间隔的边界，消除了它们之间特定关系的限制性要求，从而降低了保守性。第三，提出了一种新的基于lmi的优化算法，以最大化吸引区域（ROA）的估计，与传统方法相比，有效地权衡了计算复杂度，显著降低了保守性。利用MATLAB LMI工具箱进行了数值仿真，验证了该方法的有效性和优越性。

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引用次数: 0

New results on finite-time synchronization of fuzzy delayed Cohen-Grossberg neural networks with discontinuous activations 不连续激活下模糊延迟Cohen-Grossberg神经网络有限时间同步的新结果

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems

Pub Date : 2026-05-01 Epub Date: 2026-01-08 DOI: 10.1016/j.fss.2026.109762

Qian Wang , Lian Duan , Lihong Huang , Xianwen Fang

It is known that finite-time synchronization is crucial in engineering applications, such as blockchain consensus protocols. Additionally, fields such as smart grids, UAV swarms, and autonomous driving rely on finite-time synchronization to enhance the robustness and real-time performance of cooperative control, preventing performance degradation or safety risks caused by communication delays. This paper is concerned with the finite-time synchronization problem of fuzzy delayed Cohen-Grossberg neural networks (CGNNs) in which the activation functions are discontinuous. By designing delay-independent feedback controllers combined with new analytical techniques, some novel finite-time synchronization criteria are established without using the widely employed finite-time stability theory, which greatly enriches the theory of complex neurodynamics. Finally, a numerical example is provided to illustrate the effectiveness of the theoretical results.

众所周知，有限时间同步在区块链共识协议等工程应用中至关重要。此外，智能电网、无人机群和自动驾驶等领域依赖于有限时间同步来增强协同控制的鲁棒性和实时性，防止通信延迟导致的性能下降或安全风险。研究了激活函数不连续的模糊延迟Cohen-Grossberg神经网络的有限时间同步问题。通过设计与时滞无关的反馈控制器，结合新的分析技术，在不使用广泛使用的有限时间稳定性理论的情况下，建立了一些新的有限时间同步准则，极大地丰富了复杂神经动力学的理论。最后，通过数值算例验证了理论结果的有效性。

引用次数: 0

Algorithm for computing all Goldman’s fuzzy reducts 计算所有高盛模糊约简的算法

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems

Pub Date : 2026-05-01 Epub Date: 2026-01-01 DOI: 10.1016/j.fss.2025.109747

Francisco David Camacho-Gonzalez , Jesús Ariel Carrasco-Ochoa , José Francisco Martínez-Trinidad

Goldman’s fuzzy reducts are subsets of attributes that allows for preserving the discernibility capacity of the whole set of attributes, in these subsets each attribute has an associated discernibility value, which can be interpreted as the capacity the correspeponing attribute has to discern objects of different class. Computing all Goldman’s fuzzy reducts is time-consuming, and few algorithms have been proposed in the literature. For these reasons, this paper introduces a new algorithm for computing all Goldman’s fuzzy reducts. The proposed algorithm traverses the search space following a new ordering and applies pruning properties, introduced in this paper, that help avoid exhaustively evaluating the reduct definition and discarding subsets. Additionally, we introduced a concept of density for simplified non-Boolean discernibility matrices that allows a density-based characterization of the algorithms’ performance. The proposed algorithm is evaluated and compared against state-of-the-art algorithms on synthetic and real decision systems. From our experiments, we determine the matrix type regarding density, where our algorithm performs the best.

高盛的模糊约简是属性的子集，它允许保留整个属性集的可分辨能力，在这些子集中，每个属性都有一个相关的可分辨值，这可以解释为相应属性识别不同类别对象的能力。计算所有高盛的模糊约简是耗时的，并且在文献中提出的算法很少。基于这些原因，本文引入了一种计算所有Goldman模糊约简的新算法。提出的算法按照新的排序遍历搜索空间，并应用本文介绍的修剪属性，这有助于避免穷尽地评估约简定义和丢弃子集。此外，我们为简化的非布尔可别性矩阵引入了密度的概念，允许基于密度的算法性能表征。在综合决策系统和实际决策系统上对该算法进行了评价和比较。从我们的实验中，我们确定了关于密度的矩阵类型，我们的算法在密度方面表现最好。

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引用次数: 0

Absolutely continuous copulas with a given curvilinear section 具有给定曲线截面的绝对连续的联系线

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems

Pub Date : 2026-05-01 Epub Date: 2026-01-06 DOI: 10.1016/j.fss.2026.109764

Yao Ouyang , Qiuyu Cao , Hua-Peng Zhang

Given a curvilinear section, there are three known copulas. We prove that two of them are always singular and the third one is also singular under some mild condition. With the aid of the two always singular copulas and by employing the rectangular patchwork of copulas, we explore when there exists an absolutely continuous copula with a given curvilinear section. Several sufficient conditions and one necessary condition are discovered for the existence problem. The Durante-Jaworski theorem is retrieved when the curvilinear section reduces to a diagonal section.

给定一个曲线截面，有三个已知的copula。我们证明了其中两个总是奇异的，而第三个在一定的条件下也是奇异的。借助两个总是奇异的联结，利用联结的矩形拼接，探讨了给定曲线截面上存在绝对连续联结的条件。发现了存在性问题的几个充分条件和一个必要条件。当曲线截面简化为对角线截面时，恢复了Durante-Jaworski定理。

引用次数: 0

Evolving interval type-2 fuzzy state-space identification using PSO-tuned footprint of uncertainty and filtered markov parameters 基于pso优化的不确定性足迹和滤波马尔可夫参数的演化区间2型模糊状态空间辨识

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems

Pub Date : 2026-05-01 Epub Date: 2026-01-05 DOI: 10.1016/j.fss.2025.109751

Luís Miguel Magalhães Torres , Ginalber Luiz de Oliveira Serra

This paper presents an evolving state-space interval type-2 Takagi-Sugeno identification algorithm. The methodology integrates an online particle swarm optimization scheme, which tunes the footprint of uncertainty by minimizing a coverage-width objective, with a filtering-based recursive estimation of fuzzy Markov parameters that provides unbiased consequent parameters under non-white noise. The antecedent structure is updated online through density-based rule creation, adaptation, and pruning. The approach is validated on a nonlinear benchmark with abrupt and gradual parameter changes and on a two-degrees-of-freedom helicopter. Compared to state-of-the-art methods, the proposed algorithm achieves lower error and higher-quality prediction intervals.

提出了一种演化状态空间区间2型Takagi-Sugeno识别算法。该方法集成了在线粒子群优化方案，该方案通过最小化覆盖宽度目标来调整不确定性的足迹，以及基于滤波的模糊马尔可夫参数递归估计，该参数在非白噪声下提供无偏的结果参数。通过基于密度的规则创建、调整和修剪，在线更新先行结构。在具有突变和渐变参数变化的非线性基准和二自由度直升机上对该方法进行了验证。与现有方法相比，该算法误差小，预测区间质量高。

引用次数: 0

A Gödel logic descriptor for chains of regular languages 用于正则语言链的Gödel逻辑描述符

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems

Pub Date : 2026-05-01 Epub Date: 2025-12-29 DOI: 10.1016/j.fss.2025.109749

Stefano Aguzzoli , Brunella Gerla , Sarah Nastasi

Fortresses constitute a class of language descriptors completely based only on two fundamental concepts of propositional logic: the notion of logical consequence and the notion of substitution. Fortresses based on classical propositional logic precisely recognise the class of regular languages.

In this paper, we characterise the formal languages obtained by replacing, in fortresses, the notion of logical consequence in classical logic with the one in Gödel infinitely-valued logic. We prove that fortresses in Gödel logic exactly recognise finite chains of inclusions of regular languages. To prove this, we make use of a Stone’s type categorical duality between the algebraic semantics of Gödel propositional logic and the category of finite forests and open maps.

堡垒构成了一类完全基于命题逻辑的两个基本概念的语言描述符：逻辑推论概念和替换概念。基于经典命题逻辑的堡垒算法能够精确识别规则语言的类别。在本文中，我们通过用Gödel无限值逻辑中的逻辑推论概念代替经典逻辑中的逻辑推论概念来描述形式语言的特征。我们证明了Gödel逻辑中的堡垒能够准确地识别正则语言包含的有限链。为了证明这一点，我们利用了Gödel命题逻辑的代数语义与有限森林和开放映射的范畴之间的Stone型范畴对偶性。

引用次数: 0

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