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Antagonistic-information-dependent integral-type event-trigger scheme for bipartite synchronization of cooperative-competitive neural networks and its application 用于合作竞争神经网络两端同步的拮抗-信息依赖积分型事件触发方案及其应用

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-08 DOI: 10.1016/j.ins.2024.121617

Xindong Si , Yingjie Fan , Zhen Wang

This paper focuses on the bipartite synchronization problem for cooperative-competitive neural networks (CCNNs) by using an antagonistic-information-dependent integral-type event-trigger scheme. Here, the designed antagonistic-information implies that both the cooperation and competition interactions of CCNNs are utilized to design trigger scheme. First, the signed digraph theory, in the presence of structurally balanced topology, is used to describe the antagonistic interactions among neuron nodes. On this basis, such a trigger scheme consisting of antagonistic-information and integral term is proposed to relax communication burden, which can remember the evolution information of CCNNs dynamic process. Meanwhile, the discontinuity of event-triggered scheme can avoid the occurrence of Zeno behavior directly without complicated mathematical analysis. Then, an important lemma is derived to facilitate bipartite synchronization problem. By constructing appropriate Lyapunov function, two novel bipartite synchronization criteria are developed by utilizing the hybrid Lyapunov theories, new lemma, and inequality techniques. At last, an application and an effective example are presented to illustrate the validity and advantage of the proposed method.

本文通过使用依赖于拮抗信息的积分型事件触发方案，重点研究了合作竞争神经网络（CCNN）的两端同步问题。在这里，所设计的拮抗信息意味着利用 CCNN 的合作与竞争相互作用来设计触发方案。首先，在拓扑结构平衡的情况下，使用签名数字图理论来描述神经元节点之间的拮抗相互作用。在此基础上，提出了由拮抗信息和积分项组成的触发方案，以减轻通信负担，从而记住 CCNN 动态过程的演化信息。同时，事件触发方案的不连续性可以直接避免芝诺行为的发生，而无需复杂的数学分析。然后，推导出了一个重要的两端同步问题。通过构建适当的 Lyapunov 函数，利用混合 Lyapunov 理论、新的 Lemma 和不等式技术，提出了两个新的双端同步准则。最后，介绍了一个应用和有效实例，以说明所提方法的有效性和优势。

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引用次数: 0

A context-enhanced neural network model for biomedical event trigger detection 用于生物医学事件触发检测的语境增强型神经网络模型

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-08 DOI: 10.1016/j.ins.2024.121625

Zilin Wang , Yafeng Ren , Qiong Peng , Donghong Ji

As an important component of biomedical event extraction, biomedical event trigger detection has received extensive research attention in recent years. Most studies focus on designing various models or features according to the original text itself, but fail to leverage contextual information of the original text from external knowledge base such as Wikipedia, which is publicly available. To address the issue, we propose a context-enhanced neural network model that automatically integrates the related information from external knowledge base for biomedical event trigger detection. Specifically, the proposed model first extracts the related context of the original text from external knowledge base. Then the original text and its context are sequentially fed into the BERT embedding layer and Transformer convolution layer to learn high-level semantic representation. Finally, the probability of possible tags is calculated using the CRF layer. Experimental results on the MLEE dataset show our proposed model achieves 86.83% F1 score, outperforming the existing methods and context-enhanced baseline systems significantly. Experimental analysis also indicates the effectiveness of contextual information for trigger detection in biomedical domain.

作为生物医学事件提取的重要组成部分，生物医学事件触发检测近年来受到了广泛的研究关注。大多数研究都侧重于根据原文本身设计各种模型或特征，但却未能从维基百科等公开的外部知识库中充分利用原文的上下文信息。针对这一问题，我们提出了一种上下文增强神经网络模型，该模型可自动整合外部知识库中的相关信息，用于生物医学事件触发检测。具体来说，该模型首先从外部知识库中提取原文的相关上下文。然后，将原文及其上下文依次输入 BERT 嵌入层和 Transformer 卷积层，以学习高级语义表征。最后，使用 CRF 层计算可能标签的概率。在 MLEE 数据集上的实验结果表明，我们提出的模型获得了 86.83% 的 F1 分数，明显优于现有方法和上下文增强基线系统。实验分析还表明了上下文信息在生物医学领域触发检测中的有效性。

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引用次数: 0

Identification of a class of singular Boolean control networks 一类奇异布尔控制网络的识别

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-08 DOI: 10.1016/j.ins.2024.121627

Rong Zhao , Jun-e Feng , Qingchun Meng , Biao Wang

System identification, recognized as an inverse control problem, is a significant aspect of modern control theory. This study focuses on addressing the identification problem related to a specific category of singular Boolean networks (SBNs) and singular Boolean control networks (SBCNs). The introduction of two novel concepts, namely the admissibility and solvability matrices, enables the establishment of conditions for determining the existence and uniqueness of solutions for SBNs and SBCNs. Then criteria are deduced to identify the number of dynamic equations. Based on observability, controllability and detectability, several conditions are presented to characterize identification. Among them, two crucial results show: When the solution to an SBN or SBCN is unique, the SBN is identifiable if and only if it is observable, and the SBCN is identifiable if and only if it is O1-observable, which is the most general type of observability. Besides, effective algorithms are devised to implement identification. Furthermore, the study delves into the normalization issue using the admissibility matrix, which provides a possibility to reduce the identified SBN or SBCN to a lower-order BN or BCN.

系统辨识被视为逆控制问题，是现代控制理论的一个重要方面。本研究侧重于解决与奇异布尔网络（SBN）和奇异布尔控制网络（SBCN）的特定类别相关的识别问题。通过引入两个新概念，即可接受性矩阵和可解性矩阵，建立了确定 SBN 和 SBCN 解的存在性和唯一性的条件。然后推导出确定动态方程数量的标准。在可观测性、可控性和可探测性的基础上，提出了几个条件来描述识别。其中有两个关键结果：当 SBN 或 SBCN 的解是唯一的时，只有当 SBN 是可观测的时，它才是可识别的；只有当 SBCN 是 O1-observable 时，它才是可识别的，而 O1-observable 是最一般的可观测类型。此外，还设计了有效的算法来实现识别。此外，研究还利用可接纳矩阵深入探讨了归一化问题，这为将已识别的 SBN 或 SBCN 简化为低阶 BN 或 BCN 提供了可能。

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引用次数: 0

Dynamic evidence fusion neural networks with uncertainty theory and its application in brain network analysis 具有不确定性理论的动态证据融合神经网络及其在脑网络分析中的应用

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-08 DOI: 10.1016/j.ins.2024.121622

Weiping Ding , Tao Hou , Jiashuang Huang , Hengrong Ju , Shu Jiang

Deep learning has demonstrated significant potential and advantages, achieving notable success in the medical field, particularly in the application of brain network analysis. However, most models ignore the uncertainty caused by inconsistent view quality and fail to effectively leverage the potential correlations and temporal sequences present in multi-view data, preventing neural networks from fully showcasing their strengths. To this end, this paper proposes dynamic evidence fusion neural networks (DEF-NNs) with uncertainty theory, and applies it to brain network analysis. Our model is established within a multi-view learning framework that considers the functional connections under each window as a view. We employ a dynamic evidence learning module to capture the evidence for each time window of the dynamic brain network, utilizing three distinct convolutional filters to extract feature maps. Then, a dynamic evidence fusion mechanism is designed and a dynamic trust function is constructed according to the temporal nature of dFC data. The evidence generated by multiple windows is fused at the decision level of classification, dealing with the uncertainty caused by inconsistent view quality and improving the classification performance. We verified the effectiveness of DEF-NNs through comparison with advanced algorithms on three schizophrenia datasets, and the results show that DEF-NNs significantly improved the classification performance of brain disease diagnosis tasks.

深度学习已显示出巨大的潜力和优势，在医疗领域取得了显著的成功，尤其是在脑网络分析的应用方面。然而，大多数模型都忽略了视图质量不一致所带来的不确定性，未能有效利用多视图数据中潜在的相关性和时间序列，使神经网络无法充分展示其优势。为此，本文提出了具有不确定性理论的动态证据融合神经网络（DEF-NN），并将其应用于脑网络分析。我们的模型建立在多视图学习框架内，将每个窗口下的功能连接视为一个视图。我们采用动态证据学习模块来捕捉动态脑网络每个时间窗口的证据，利用三种不同的卷积滤波器来提取特征图。然后，我们设计了一种动态证据融合机制，并根据 dFC 数据的时间特性构建了动态信任函数。多窗口生成的证据在分类的决策层被融合，处理了视图质量不一致带来的不确定性，提高了分类性能。我们在三个精神分裂症数据集上通过与先进算法的比较验证了 DEF-NNs 的有效性，结果表明 DEF-NNs 显著提高了脑疾病诊断任务的分类性能。

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引用次数: 0

High-dimensional expensive optimization by Kriging-assisted multiobjective evolutionary algorithm with dimensionality reduction 利用克里金辅助多目标进化算法进行高维昂贵优化并降低维度

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-08 DOI: 10.1016/j.ins.2024.121620

Zeyuan Yan , Yuren Zhou , Xiaoyu He , Chupeng Su , Weigang Wu

Surrogate-assisted multi-objective evolutionary algorithms (SA-MOEAs) have made significant progress in solving expensive multi- and many-objective optimization problems. However, most of them perform well in low-dimensional settings but often struggle with high-dimensional problems. The main reason is that some techniques used in SA-MOEAs, like the Kriging model, are ineffective in exploring high-dimensional search spaces. As a result, this research investigates frameworks incorporating dimensionality reduction techniques to conduct modeling and optimization tasks on dimensionality reduction decision spaces. This article uses a singular value decomposition method to map the high-dimensional decision space into a low-dimensional one, then employs a feature fusion strategy to combine low-dimensional features with high-dimensional ones for better representation. Subsequently, these low-dimensional features are used to train the Kriging-based surrogates to select promising solutions within a limited number of function evaluations. In addition, this article provides two types of evolutionary modes to balance exploration and exploitation. Experimental results demonstrate the effectiveness of the proposed SA-MOEA compared to several state-of-the-art algorithms.

代理辅助多目标进化算法（SA-MOEAs）在解决昂贵的多目标和多目标优化问题方面取得了重大进展。然而，它们中的大多数在低维环境中表现出色，但在处理高维问题时往往举步维艰。主要原因是 SA-MOEAs 中使用的一些技术，如克里金模型，在探索高维搜索空间时效果不佳。因此，本研究探讨了结合降维技术的框架，以便在降维决策空间上执行建模和优化任务。本文使用奇异值分解法将高维决策空间映射为低维决策空间，然后采用特征融合策略将低维特征与高维特征相结合，以获得更好的表征效果。然后，利用这些低维特征来训练基于 Kriging 的代用模型，从而在有限的函数评估次数内选出有希望的解决方案。此外，本文还提供了两种进化模式，以平衡探索和利用。实验结果表明，与几种最先进的算法相比，所提出的 SA-MOEA 非常有效。

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引用次数: 0

Configuration of liveness-enforcing initial marking with the minimum resources for resource allocation systems 为资源分配系统配置最少资源的增强活泼性初始标记

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-08 DOI: 10.1016/j.ins.2024.121623

Yanxiang Feng , Sida Ren , Keyi Xing , Yikang Yang , MengChu Zhou

The enforcement of liveness is crucial for Petri nets models of resource allocation systems (RASs). It is interesting yet very challenging to establish an initial marking for Petri net plants so that the net is live. Such an initial marking is referred to as liveness-enforcing initial marking (LIM). Despite existing literature presenting various LIMs, no studies have addressed the issue of minimizing the number of resources in an LIM. This work focuses on designing an LIM with the minimum resources (LIM-MR) for a class of Petri nets called systems of sequential systems with shared resources (S⁴PRs) by assigning a token capacity to each resource place, such that the sum of all involved resources is minimized. This work first establishes a kind of necessary and sufficient liveness condition for S⁴PR, which is then encoded into a series of variables and constraints in a mixed-integer programming (MIP) formulation. Although LIM-MR may not be unique, solving the proposed MIP formulation can obtain at least one LIM-MR for S⁴PR under consideration. The experimental results show the solvability of this approach for S⁴PRs.

对于资源分配系统（RAS）的 Petri 网模型来说，执行有效性至关重要。为 Petri 网植物建立一个初始标记，使其成为活网，是一件有趣但极具挑战性的事情。这种初始标记被称为活泼强化初始标记（LIM）。尽管现有文献介绍了各种 LIM，但还没有研究涉及如何最大限度地减少 LIM 中的资源数量。本研究的重点是为一类 Petri 网（即具有共享资源的连续系统（S4PRs））设计具有最少资源的 LIM（LIM-MR），方法是为每个资源位置分配一个标记容量，从而使所有涉及资源的总和最小化。这项工作首先为 S4PR 建立了一种必要且充分的有效性条件，然后将其编码为混合整数编程 (MIP) 公式中的一系列变量和约束条件。虽然 LIM-MR 不一定是唯一的，但通过求解所提出的 MIP 公式，可以为所考虑的 S4PR 得到至少一个 LIM-MR。实验结果表明了这种方法对 S4PR 的可解性。

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引用次数: 0

Linear fuzzy partial differential equations for A-linearly correlated fuzzy processes A 线性相关模糊过程的线性模糊偏微分方程

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-08 DOI: 10.1016/j.ins.2024.121629

M. Shahidi, L.C. Barros, E. Esmi

In this paper, we deal with linear fuzzy partial differential equations (FPDEs) whose solutions correspond to A-linearly correlated fuzzy processes. More precisely, we investigate general second-order linear fuzzy partial differential equations and provide solutions for two essential cases: the fuzzy advection equation and the fuzzy wave-like equation with fuzzy velocity terms. One of the advantages of our approach is that these FPDEs can be converted into a classical system of partial differential equations (PDEs). Thus, any suitable and appropriate method to solve PDEs can be applied to solve this classical system and, eventually leading to the derivation of a fuzzy solution. Finally, we provide some examples to demonstrate our results.

本文讨论线性模糊偏微分方程 (FPDE)，其解与 A 线性相关模糊过程相对应。更确切地说，我们研究了一般的二阶线性模糊偏微分方程，并提供了两种基本情况的解法：模糊平流方程和带有模糊速度项的模糊类波方程。我们的方法的优点之一是，这些 FPDE 可以转换成经典的偏微分方程（PDE）系统。因此，任何合适的 PDE 求解方法都可以用于求解这个经典系统，并最终推导出模糊解。最后，我们提供了一些例子来展示我们的成果。

引用次数: 0

Event-based adaptive neural resilient formation control for MIMO nonlinear MASs under actuator saturation and denial-of-service attacks 执行器饱和与拒绝服务攻击下的多输入多输出非线性 MAS 的基于事件的自适应神经弹性编队控制

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-08 DOI: 10.1016/j.ins.2024.121619

Xiangjun Wu , Ning Xu , Shuo Ding , Xudong Zhao , Ben Niu , Wencheng Wang

This paper focuses on the distributed event-triggered adaptive neural resilient time-varying formation control problem for a class of multiple-input multiple-output nonlinear multi-agent systems, where all network communication links between agents are subjected to denial-of-service (DoS) attacks simultaneously. A second-order resilient time-varying formation estimator is designed to obtain the unknown leader information in DoS attack active intervals. Meanwhile, a state-triggering mechanism (STM) is designed to save system communication resources. Nevertheless, the STM can lead to virtual control laws being non-differentiable. To circumvent the problem, we first design an adaptive neural resilient formation control scheme. Then, based on the adaptive neural resilient formation control scheme, we replace continuous states with intermittent ones. By utilizing a dynamic filtering technique, an event-based adaptive neural resilient formation control scheme is designed. The key technology of control scheme design is to establish an improved first-order auxiliary system to deal with the negative impact of actuator saturation. It is proved that formation tracking errors can converge to a residual set around zero, and all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results are presented to show the effectiveness of the control scheme.

本文主要研究一类多输入多输出非线性多代理系统的分布式事件触发自适应神经弹性时变编队控制问题，在该系统中，代理之间的所有网络通信链路都同时受到拒绝服务（DoS）攻击。设计了一种二阶弹性时变编队估计器，用于获取 DoS 攻击活动时间间隔内的未知领导者信息。同时，还设计了一种状态触发机制（STM）来节省系统通信资源。然而，STM 可能会导致虚拟控制法则的不可分性。为了规避这一问题，我们首先设计了一种自适应神经弹性编队控制方案。然后，在自适应神经弹性编队控制方案的基础上，我们用间歇状态取代连续状态。利用动态滤波技术，设计出基于事件的自适应神经弹性编队控制方案。控制方案设计的关键技术是建立一个改进的一阶辅助系统，以应对执行器饱和带来的负面影响。研究证明，编队跟踪误差可以收敛到零附近的残差集，闭环系统中的所有信号都是半全局均匀终界的。最后，仿真结果表明了控制方案的有效性。

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引用次数: 0

Associative operations on the poset of closed intervals of a given lower-bounded poset 给定下界正集的闭区间正集的关联运算

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-08 DOI: 10.1016/j.ins.2024.121630

Yexing Dan, Xiaodong Pan

Under the inclusion order, our focus lies in exploring associative binary operations on the poset of closed intervals of a given lower-bounded poset. Initially, we propose a method for extending an increasing and associative binary operation on a given lower-bounded poset to the poset of closed intervals of that given lower-bounded poset. It involves the use of an interior operator between that given lower-bounded poset and one of its subposets, alongside another appropriately chosen increasing and associative binary operation on the same lower-bounded poset. Subsequently, we explore conditions under which this extended operation can facilitate the constructions of t-conorms and t-norms on the poset of closed intervals of a given bounded poset. Lastly, we delve into the properties of the constructed t-conorms and t-norms, specifically addressing the Archimedean property and the existence of idempotent and nilpotent elements.

在包含令下，我们的重点是探索给定下界正集的闭区间正集上的关联二元运算。起初，我们提出了一种方法，将给定下界正集上的递增关联二元运算扩展到给定下界正集的封闭区间正集。这涉及在给定的下界正集和它的一个子集之间使用一个内部算子，同时在同一下界正集上使用另一个适当选择的递增和关联二元运算。随后，我们将探讨在哪些条件下，这种扩展运算可以促进给定有界正集的闭区间正集上的 t 准则和 t 准则的构造。最后，我们深入探讨所构造的 t-conorms 和 t-norms 的性质，特别是阿基米德性质以及幂等元素和零等元素的存在。

引用次数: 0

Regret-based domination three-way decision-making model with circular spherical fuzzy Mahalanobis distance 基于遗憾的支配三方决策模型与环形球面模糊 Mahalanobis 距离

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-07 DOI: 10.1016/j.ins.2024.121616

Shahzaib Ashraf , Chiranjibe Jana , Wania Iqbal , Muhammet Deveci

This article proposes a revolutionary three-way multi-attribute model in response to the increased complexity and risk in decision-making processes. This model combines three-way decision rules with the influential behavioral theories of regret and prospect. To address uncertainties in evaluation data, a circular spherical fuzzy set is seamlessly integrated into the information system, noting that this method can also be smoothly applied to disc spherical fuzzy sets (D-SFSs). Moreover, a pioneering method utilizing correlation coefficients and variances is introduced for attribute weighting. The conventional Mahalanobis distance is extended to a circular, spherical, fuzzy Mahalanobis distance. A novel approach is developed to estimate conditional probabilities based on the distinct regret-based dominating and rejoice-based dominating relations in circular spherical fuzzy sets (C-SFSs). Additionally, three scoring systems based on prospect theory yield despondent, expectant, and balanced decision-making strategies and provide priceless insights into various decision-maker mindsets. Furthermore, to illustrate the practical application of these methods with D-SFSs information, an example has been solved utilizing D-SFSs.

本文提出了一个革命性的三向多属性模型，以应对决策过程中日益增加的复杂性和风险。该模型将三向决策规则与具有影响力的后悔和前景行为理论相结合。为了解决评估数据中的不确定性，本文将圆球形模糊集无缝集成到信息系统中，并指出这种方法也可顺利应用于圆盘球形模糊集（D-SFS）。此外，还引入了一种利用相关系数和方差进行属性加权的开创性方法。传统的 Mahalanobis 距离被扩展为圆形、球形、模糊 Mahalanobis 距离。根据环形球面模糊集（C-SFS）中不同的基于遗憾的支配关系和基于欣喜的支配关系，开发了一种估算条件概率的新方法。此外，基于前景理论的三种评分系统得出了绝望型、期望型和平衡型决策策略，为了解决策者的各种心态提供了无价之宝。此外，为了说明这些方法与 D-SFSs 信息的实际应用，我们利用 D-SFSs 解决了一个实例。

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引用次数: 0