Article
Computer Science, Artificial Intelligence
Hong Sang, Jun Zhao
Summary: This article investigates the energy-to-peak state estimation problem for a class of switched neutral neural networks, introducing piecewise time-dependent Lyapunov-Krasovskii functional and slow switching mechanism. The coexistence of switching and sampling actions causes asynchronous phenomena, where the designed state estimator exponentially tracks the true value of the neural state.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Mathematics, Interdisciplinary Applications
Binbin Gan, Hao Chen, Biao Xu, Wei Kang
Summary: By constructing an appropriate Lyapunov functional, this paper obtains a novel delay-independent stability criterion for neutral-type Cohen-Grossberg neural networks with multiple time delays. The proposed method reduces computational complexity and conservatism compared to previous literature.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Engineering, Mechanical
G. Nagamani, B. Adhira, G. Soundararajan
Summary: This paper discusses the design of a non-fragile state estimator for a class of discrete-time neural networks, including uncertainties and time-varying delay components, to study a robust extended dissipativity criterion. The proposed approach involves constructing a Lyapunov-Krasovskii functional and expressing theoretical results in terms of linear matrix inequalities. Numerical examples, such as a quadruple tank process system model, have been used to illustrate the applicability and effectiveness of the proposed theoretical results.
NONLINEAR DYNAMICS
(2021)
Article
Mathematics, Applied
Shuang Li, Xiao-mei Wang, Hong-ying Qin, Shou-ming Zhong
Summary: This paper investigates the synchronization problem of neutral-type quaternion-valued neural networks with mixed delays. Linear and nonlinear feedback controllers are proposed to study global and finite-time synchronization, respectively. Sufficient conditions for synchronization are obtained based on appropriate functional and synchronization techniques, with less conservative results than existing ones shown through numerical examples.
Article
Computer Science, Information Systems
Xin Chang, Qinkun Xiao, Yilin Zhu, Jielei Xiao
Summary: This study explores the theory and application of fractional-order neural networks, focusing on the asymptotic stability with Riemann-Liouville derivatives. The proposed stability criteria are based on Lyapunov and LMI methods, showcasing the uniqueness of solutions using matrix analysis and contraction mapping theory for delayed systems. The results provide two sets of asymptotic stability criteria for fractional-order neural networks, verified through numerical simulations.
Article
Automation & Control Systems
Xiangli Jiang, Guihua Xia, Zhiguang Feng, Zhengyi Jiang, Jianbin Qiu
Summary: This article tackles the reachable set estimation problem for a class of Markovian jump neutral-type neural networks (MJNTNNs) with bounded disturbances and time-varying delays. A novel stochastic Lyapunov-Krasovskii functional is constructed using the delay partitioning method, and some sufficient conditions for network stability are obtained.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Mathematics, Applied
Shuting Chen, Ke Wang, Jiang Liu, Xiaojie Lin
Summary: This paper investigates a class of delayed Cohen-Grossberg-type bi-directional associative memory neural networks with impulses, and presents sufficient conditions to ensure the existence and stability of periodic solutions for the impulsive neural network systems. A simulation example is conducted to demonstrate the efficiency of the theoretical results.
Article
Chemistry, Physical
Chunyang Liu, Ying Shen
Summary: This study utilizes the 3D U-Net network in deep learning techniques for foreground subtraction in cosmological signals. By adding residual modules and considering system effects, better consistency results have been achieved.
JOURNAL OF ALLOYS AND COMPOUNDS
(2023)
Article
Mathematics, Applied
Ramalingam Sriraman, Grienggrai Rajchakit, Oh-Min Kwon, Sang-Moon Lee
Summary: This paper aims to explore the global stability of Cohen-Grossberg Clifford-valued neutral-type neural network models with time delays. By decomposing the Clifford-valued system into real-valued systems and constructing an appropriate Lyapunov functional, some sufficient criteria for the global stability of the network models have been established, unaffected by neutral delay and time delay values.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2022)
Article
Mathematics, Applied
Guo-Quan Liu, Shu-Min Zhou, Yue-Zhong Li, Tong-Chen Cai
Summary: This paper introduces a new class model for neutral-type BAM-NNS with infinite distributed delay, and provides stability criteria based on LKF theory and LMI method. Numerical examples support the validity and less conservativeness of the proposed criteria, which are beneficial for actual system design.
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
(2021)
Article
Computer Science, Theory & Methods
Chaouki Aouiti, Qing Hui, Hediene Jallouli, Emmanuel Moulay
Summary: This paper introduces a novel approach to address the fixed-time stabilization of fuzzy neutral-type inertial neural networks with time-varying delay, designing two different feedback control laws and providing simulation examples to validate the proposed theoretical results.
FUZZY SETS AND SYSTEMS
(2021)
Article
Computer Science, Information Systems
Li Wan, Qinghua Zhou
Summary: This paper addresses the exponential stability of a more general class of neutral-type Cohen-Grossberg neural networks, providing sufficient conditions to ensure the existence, uniqueness, and stability of the equilibrium point of the neural system. The conditions are easy to verify and guarantee global asymptotic stability, with two remarks indicating that they are less conservative than previous results. Two instructive examples are also given to demonstrate the effectiveness of the theoretical results and compare the stability conditions with previous findings.
Article
Mathematics, Interdisciplinary Applications
Nattakan Boonsatit, Santhakumari Rajendran, Chee Peng Lim, Anuwat Jirawattanapanit, Praneesh Mohandas
Summary: The issue of adaptive finite-time cluster synchronization for neutral-type coupled complex-valued neural networks with mixed delays is examined in this research. A new adaptive control technique is developed to achieve finite-time synchronization of the networks. The effectiveness of the proposed method is demonstrated through simulation studies.
FRACTAL AND FRACTIONAL
(2022)
Article
Automation & Control Systems
Arumugam Karnan, Gnaneswaran Nagamani
Summary: This article aims to design a nonfragile state estimator for neural networks with time-varying delay under the event-triggered mechanism, addressing the common issues of gain variations and lack of network resources in large-scale networks. By utilizing the nonfragile paradigm and an event-triggered mechanism, an estimator is being developed to consider both additive and multiplicative structured gain variations. Sufficient conditions are derived using the Lyapunov-Krasovskii functional method and linear matrix inequality technique, providing explicit expressions of the estimator gain matrix and triggering matrix.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Ozlem Faydasicok, Sabri Arik
Summary: This research article aims to conduct a new Lyapunov stability analysis of a special model of Cohen-Grossberg neural networks with multiple delay terms. The obtained stability results are independent of the time delay terms and are characterized by the interconnection parameters. A numerical example demonstrates the advantages and novelties of these global stability results compared to previously reported conditions.
Article
Computer Science, Artificial Intelligence
Qi Chang, Ju H. Park, Yongqing Yang
Summary: This article studies memristive neural networks with multiple time delays, focusing on both cooperative and competitive relationships. It proposes methods for achieving finite-time and fixed-time bipartite synchronization using controllers, mathematical techniques, and numerical examples to demonstrate their correctness and applicability.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuxiao Lian, Jianwei Xia, Ju H. Park, Wei Sun, Hao Shen
Summary: This article focuses on the output feedback control of a nonlinear system with unknown control directions, unknown Bouc-Wen hysteresis, and unknown disturbances. The design obstacles caused by these unknown factors are eliminated through the use of linear state and coordinate transformations, avoiding the need for high-frequency oscillating Nussbaum function. A novel nonlinear disturbance observer is designed to handle unknown disturbances, which has a simple structure, low coupling, and easy implementation. An output feedback controller is devised using neural networks and backstepping technology, ensuring bounded closed-loop signals and convergence of system output, state observation error, and disturbance observation error. Simulation verification using numerical examples and a Nomoto ship model illustrates the effectiveness of the proposed scheme.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Zhihao Liu, Yuanyuan Shang, Timing Li, Guanlin Chen, Yu Wang, Qinghua Hu, Pengfei Zhu
Summary: Multi-drone multi-target tracking aims to detect and track targets across multiple drones and associate their identities, overcoming the limitations of single-drone object tracking. In this study, we introduce a novel occlusion-aware multi-drone multi-target tracking dataset called MDMT. It consists of 88 video sequences with 39,678 frames and includes 11,454 different IDs of persons, bicycles, and cars. We also propose a Multi-matching Identity Authentication network (MIA-Net) that effectively addresses the challenges of identity association and target occlusion in multi-drone multi-target tracking.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Engineering, Electrical & Electronic
Teng-Fei Ding, Kun-Ting Xu, Ming-Feng Ge, Ju H. Park, Chang-Duo Liang
Summary: In this paper, a fast fixed-time control algorithm is proposed to solve the output multi-formation tracking problem of networked autonomous surface vehicles. The algorithm divides the vehicles into interconnected subnetworks and ensures that the outputs form desired geometric formations through local interactions. By using a time-related function and a nonsingular fixed-time sliding surface, the algorithm achieves fast fixed-time convergence independent of initial conditions.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Automation & Control Systems
Guoliang Chen, Jianwei Xia, Ju H. Park, Hao Shen, Guangming Zhuang
Summary: This article presents a method for designing an asynchronous aperiodic sampled-data controller for a class of Markov jump systems with switching transition rate. By using T-S fuzzy models and constructing novel Lyapunov functionals, stability criteria and dissipativity criteria are derived. An asynchronous aperiodic sampled-data controller is designed for fuzzy Ito stochastic Markov jump systems. The effectiveness and lower conservativeness of the proposed method are illustrated by fuzzing two nonlinear system models.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Wenhai Qi, Yanjing Yi, Ju H. Park, Huaicheng Yan, Jun Cheng
Summary: This paper investigates the application of protocol-based control in positive Markovian switching models subject to actuator faults and deception attacks. A Bernoulli distribution is used to describe random deception attacks. An event-triggered protocol is constructed based on a 1-norm to relate to the error signal and state signal, considering the positivity of Markovian switching models. Exponentially stochastic stability conditions are established under the event-triggered protocol by developing a linear copositive Lyapunov function approach to handle actuator faults and random cyber attacks. Furthermore, a non-fragile control law combined with the event-triggered protocol is proposed to achieve exponential stochastic stability of the corresponding system through matrix decomposition strategy and linear programming. Finally, a data communication network model is provided to demonstrate the effectiveness of the proposed controller design.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Computer Science, Artificial Intelligence
Shen Yan, Zhou Gu, Ju H. Park, Xiangpeng Xie
Summary: In this article, the design of fuzzy synchronization controller for T-S fuzzy neural networks with distributed time-varying delay and probabilistic network communication delay is studied. A more general model containing a distributed delay kernel is considered. By utilizing the probability distribution of random communication delays, a distributed but deterministic delay model is established. New sufficient conditions for the existence of a synchronization controller are presented by developing a new Lyapunov-Krasovskii functional related to the distributed delay kernels and using an integral inequality. Numerical simulation and an application of encrypting the image are carried out to illustrate the effectiveness of the developed strategy.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Qidong Liu, Yue Long, Tieshan Li, Ju Hyun Park, C. L. Philip Chen
Summary: This article investigates the fault detection problem of unmanned marine vehicles (UMVs) under the influence caused by replay attacks. A Takagi-Sugeno (T-S) fuzzy system is used to model the dynamics of the UMV, and a switching-type attack tolerant fault detection filter is designed to consider possible replay attacks. The proposed algorithm is verified by simulations.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Chang-Duo Liang, Ming-Feng Ge, Zhi-Wei Liu, Leimin Wang, Ju H. Park
Summary: This paper investigates the model-free cluster formation problem of networked marine surface vehicles under external disturbances and bounded inputs constraint. A predefined-time estimator-based hierarchical control algorithm is proposed to handle this complex problem without prior model information. The algorithm decomposes the problem into two subcontrol problems, namely, distributed predefined-time estimation and local tracking. The proposed distributed estimator algorithm allows follower vehicles to estimate the states of virtual leaders within a predefined time, and these estimators are then used to construct the local bounded model-free tracking controller. The algorithm's sufficient conditions are derived through Lyapunov stability analysis, and digital simulation experiments are conducted to verify the main results on the networked Cyber-Ships II.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
S. Anusuya, R. Sakthivel, O. M. Kwon
Summary: The prime purpose of this study is to design an output tracking control law to compensate for the effects induced by time-varying input delays and disturbances. The control protocol is developed for nonlinear systems and the nonlinearities are effectively represented using membership functions. The fuzzy-dependent dynamic control law is formulated using parallel distributed compensation strategy and extended Smith predictor approach.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Seung-Ho Kim, Seung-Hoon Lee, Myeong-Jin Park, Oh-Min Kwon, Jun-Min Park
Summary: This paper proposes improved Lyapunov-Krasovskii functionals (LKFs) for asymptotic stability of generalized neural networks (GNNs) with time-varying delays. Utilizing generalized free-weighting matrix inequality (GFWMI) and mathematical techniques, sufficient conditions dependent on the size of time delays are derived to guarantee the stability of GNNs. The augmented zero equality approach (AZEA) is applied to enhance the results and eliminate free variables. Three numerical examples demonstrate the effectiveness and less conservative results of the proposed method compared to previous research.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Automation & Control Systems
Qiaomin Xiang, Ze-Hao Wu, Ju H. Parks, Bao-Zhu Guo
Summary: This paper investigates the observability and observer design for a class of systems described by two-dimensional hyperbolic PDEs with superlinear boundary conditions that can exhibit chaos. The exact and approximate observability are proven using the method of characteristic and boundary reflection relations. Based on observability, two types of Luenberger PDE observers are designed, using boundary velocity and boundary displacement measurements respectively. Sufficient conditions are developed to guarantee the global exponential stability and global asymptotic stability of the observer error systems. Numerical simulations are conducted to validate the theoretical findings.
SIAM JOURNAL ON CONTROL AND OPTIMIZATION
(2023)
Article
Automation & Control Systems
Guopin Liu, Ju H. Park, Changchun Hua, Yafeng Li
Summary: In this article, the consensus problem of multiagent systems (MASs) based on the event-triggered control (ETC) method in the presence of unreliable networks with denial-of-service (DoS) attacks is investigated. By equipping the MAS with estimation capabilities, a hybrid dynamic event-triggered strategy (HDETS) is proposed to preserve the consensus of MASs subject to unreliable networks. The proposed strategy guarantees both consensus and Zeno-freeness under certain conditions on the frequency and duration of the DoS attacks. Furthermore, a finite time resetting condition is presented to illustrate the maximal intensity of DoS attacks that the MASs can tolerate, with verification through a spacecraft formation simulation example.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Deqiang Zeng, Ruimei Zhang, Ju H. Park, Guo-Cheng Wu, Kaibo Shi, Xiangpeng Xie
Summary: This article studies the asymptotical synchronization in mean square of reaction-diffusion neural networks (RDNNs) with random delays. A time-space sampled-data controller (TSSDC) is designed by sampling on both the time domain and spatial domain, which efficiently saves the network communication resources for RDNNs. A new processing method for the TSSDC is provided, capturing more sampling information and being more concise compared with existing methods. New mean square asymptotical synchronization criteria are established for RDNNs with random delays by constructing a sampling-dependent LKF, using the extended Poincare-Wirtinger inequality and Holder inequality, and the desired TSSDC gain is obtained. A numerical example is given to verify the effectiveness and superiority of the obtained results.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Yajuan Liu, Zhao Fang, Ju H. H. Park, Fang Fang
Summary: This article focuses on the event-triggered synchronization of delayed discrete-time chaotic neural networks with quantized effect and stochastic deception attack. An event-triggered mechanism and a logarithmic quantizer are employed to alleviate network communication and burden. A synchronization error model is introduced to integrate the impact of event-triggered scheme, quantization, and cyberattack. The proposed method is verified by numerical examples.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Mathematics, Applied
Peter Frolkovic, Nikola Gajdosova
Summary: This paper presents compact semi-implicit finite difference schemes for solving advection problems using level set methods. Through numerical tests and stability analysis, the accuracy and stability of the proposed schemes are verified.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Md. Rajib Arefin, Jun Tanimoto
Summary: Human behaviors are strongly influenced by social norms, and this study shows that injunctive social norms can lead to bi-stability in evolutionary games. Different games exhibit different outcomes, with some showing the possibility of coexistence or a stable equilibrium.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Dingyi Du, Chunhong Fu, Qingxiang Xu
Summary: A correction and improvement are made on a recent joint work by the second and third authors. An optimal perturbation bound is also clarified for certain 2 x 2 Hermitian matrices.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Pingrui Zhang, Xiaoyun Jiang, Junqing Jia
Summary: In this study, improved uniform error bounds are developed for the long-time dynamics of the nonlinear space fractional Dirac equation in two dimensions. The equation is discretized in time using the Strang splitting method and in space using the Fourier pseudospectral method. The major local truncation error of the numerical methods is established, and improved uniform error estimates are rigorously demonstrated for the semi-discrete scheme and full-discretization. Numerical investigations are presented to verify the error bounds and illustrate the long-time dynamical behaviors of the equation with honeycomb lattice potentials.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Kuan Zou, Wenchen Han, Lan Zhang, Changwei Huang
Summary: This research extends the spatial PGG on hypergraphs and allows cooperators to allocate investments unevenly. The results show that allocating more resources to profitable groups can effectively promote cooperation. Additionally, a moderate negative value of investment preference leads to the lowest level of cooperation.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Kui Du
Summary: This article introduces two new regularized randomized iterative algorithms for finding solutions with certain structures of a linear system ABx = b. Compared to other randomized iterative algorithms, these new algorithms can find sparse solutions and have better performance.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Shadi Malek Bagomghaleh, Saeed Pishbin, Gholamhossein Gholami
Summary: This study combines the concept of vanishing delay arguments with a linear system of integral-algebraic equations (IAEs) for the first time. The piecewise collocation scheme is used to numerically solve the Hessenberg type IAEs system with vanishing delays. Well-established results regarding regularity, existence, uniqueness, and convergence of the solution are presented. Two test problems are studied to verify the theoretical achievements in practice.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Qi Hu, Tao Jin, Yulian Jiang, Xingwen Liu
Summary: Public supervision plays an important role in guiding and influencing individual behavior. This study proposes a reputation incentives mechanism with public supervision, where each player has the authority to evaluate others. Numerical simulations show that reputation provides positive incentives for cooperation.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Werner M. Seiler, Matthias Seiss
Summary: This article proposes a geometric approach for the numerical integration of (systems of) quasi-linear differential equations with singular initial and boundary value problems. It transforms the original problem into computing the unstable manifold at a stationary point of an associated vector field, allowing efficient and robust solutions. Additionally, the shooting method is employed for boundary value problems. Examples of (generalized) Lane-Emden equations and the Thomas-Fermi equation are discussed.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Lisandro A. Raviola, Mariano F. De Leo
Summary: We evaluated the performance of novel numerical methods for solving one-dimensional nonlinear fractional dispersive and dissipative evolution equations and showed that the proposed methods are effective in terms of accuracy and computational cost. They can be applied to both irreversible models and dissipative solitons, offering a promising alternative for solving a wide range of evolutionary partial differential equations.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Yong Wang, Jie Zhong, Qinyao Pan, Ning Li
Summary: This paper studies the set stability of Boolean networks using the semi-tensor product of matrices. It introduces an index-vector and an algorithm to verify and achieve set stability, and proposes a hybrid pinning control technique to reduce computational complexity. The issue of synchronization is also discussed, and simulations are presented to demonstrate the effectiveness of the results obtained.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Ling Cheng, Sirui Zhang, Yingchun Wang
Summary: This paper considers the optimal capacity allocation problem of integrated energy systems (IESs) with power-gas systems for clean energy consumption. It establishes power-gas network models with equality and inequality constraints, and designs a novel full distributed cooperative optimal regulation scheme to tackle this problem. A distributed projection operator is developed to handle the inequality constraints in IESs. The simulation demonstrates the effectiveness of the distributed optimization approach.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Abdurrahim Toktas, Ugur Erkan, Suo Gao, Chanil Pak
Summary: This study proposes a novel image encryption scheme based on the Bessel map, which ensures the security and randomness of the ciphered images through the chaotic characteristics and complexity of the Bessel map.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Xinjie Fu, Jinrong Wang
Summary: In this paper, we establish an SAIQR epidemic network model and explore the global stability of the disease in both disease-free and endemic equilibria. We also consider the control of epidemic transmission through non-instantaneous impulsive vaccination and demonstrate the sustainability of the model. Finally, we validate the results through numerical simulations using a scale-free network.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Maria Han Veiga, Lorenzo Micalizzi, Davide Torlo
Summary: The paper focuses on the iterative discretization of weak formulations in the context of ODE problems. Several strategies to improve the accuracy of the method are proposed, and the method is combined with a Deferred Correction framework to introduce efficient p-adaptive modifications. Analytical and numerical results demonstrate the stability and computational efficiency of the modified methods.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
