Article
Computer Science, Artificial Intelligence
Yin Sheng, Tingwen Huang, Zhigang Zeng, Xiangshui Miao
Summary: This article investigates the Lagrange exponential stability and the Lyapunov exponential stability of memristive neural networks with discrete and distributed time-varying delays. The study uses inequality techniques, theories of the M-matrix, and the comparison strategy to consider the stability of the networks, providing less conservative methods for analyzing Lyapunov stability.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoyu Zhang, Degang Wang, Kaoru Ota, Mianxiong Dong, Hongxing Li
Summary: This article investigates the exponentially stable problem of neural networks with two additive time-varying delay components by introducing switching ideas and delay-dependent switching adjustment indicators to construct a novel set of functionals. The less conservative stability criteria with different numbers of switching modes are obtained through some switching techniques, and the effectiveness and efficiency of the presented methods are demonstrated through simulation examples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xingxing You, Songyi Dian, Rui Guo, Shengchuan Li
Summary: This paper investigates the exponential stability analysis of discrete-time quaternion-valued neural networks with leakage delay and discrete time-varying delays. The existence and uniqueness of the equilibrium point are proposed using homeomorphic mapping theorem and Cauchy-Schwarz inequality, and a delay-dependent sufficient condition is provided for exponential stability. A numerical example is used to illustrate the effectiveness of the results obtained.
Article
Automation & Control Systems
Yun Chen, Gang Chen
Summary: This paper focuses on the admissibility analysis of singular systems with periodically time-varying delays. A novel Lyapunov-Krasovskii functional (LKF) is developed by dividing the periodic delay interval into decreasing and increasing intervals, relaxing the positive definition of the LKF and utilizing the system state and time-varying delay function information. A new admissibility condition is derived by combining the newly constructed LKF and the second-order canonical Bessel-Legendre integral inequality. Two numerical examples are provided to demonstrate the superiority of the proposed method.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Qiao Chen, Xinge Liu, Xuemei Li
Summary: This paper presents an improved approach for the exponential stability of neural networks with time-varying delay, establishing a less conservative stability criterion by combining various inequalities. The effectiveness and benefits of the proposed method are illustrated through several numerical examples.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Mathematics, Applied
Qiao Chen, Xinge Liu, Peiyu Guo, Hua Liu, Xiayun Li
Summary: This paper investigates the problems of stability and H-infinity performance for discrete-time neural networks with time-varying delay, in order to develop a less conservative delay-dependent stability criterion and method for H-infinity performance analysis. By proving an improved reciprocally convex inequality and deriving a novel free-matrix-based summation inequality, two improved sufficient conditions for stability and H-infinity performance of discrete-time neural networks with time-varying delay are obtained in terms of linear matrix inequalities (LMIs).
COMPUTATIONAL & APPLIED MATHEMATICS
(2022)
Article
Mathematics, Applied
Jenjira Thipcha, Presarin Tangsiridamrong, Thongchai Botmart, Boonyachat Meesuptong, M. Syed Ali, Pantiwa Srisilp, Kanit Mukdasai
Summary: This paper investigates the robust stability analysis issue for discrete-time neural networks with interval time-varying leakage and discrete and distributed delays using a rebuilt summation inequality. A novel inequality, less conservative than the well-known Jensen inequality, is considered and applied in the context of discrete-time delay systems. Stability and passivity criteria are obtained in terms of linear matrix inequalities (LMIs) using various techniques. Numerical examples are provided to demonstrate the validity and efficiency of the theoretical findings of this research with the assistance of the LMI Control toolbox in Matlab.
Article
Mathematics, Applied
Tae H. Lee, Myeong Jin Park, Ju H. Park
Summary: This paper addresses the stability problem of neural networks with time-varying delays by introducing novel geometry-based negative conditions and constructing new augmented Lyapunov-Krasovskii functionals. A new stability criterion is derived and demonstrated through several numerical examples.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Xi-Zi Zhou, Jianqi An, Yong He, Jianhua Shen
Summary: This study investigates the stability of neural networks with time-varying delays. Novel stability conditions are derived by employing free-matrix-based inequality and introducing variable-augmented free-weighting matrices. The introduced techniques avoid the appearance of nonlinear terms of the time-varying delay. Improved criteria are obtained by combining time-varying free-weight matrices and time-varying S-Procedure. Numerical examples are provided to demonstrate the benefits of the proposed methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Chuan-Ke Zhang, Wen-Hu Chen, Cui Zhu, Yong He, Min Wu
Summary: This paper focuses on the stability analysis of discrete-time systems with time-varying delay. Unlike recent related works, this paper aims to reduce conservatism by using relatively simple Lyapunov functions with an augmented double summation term. The key point is the establishment of a delay-dependent matrix-separation-based inequality to estimate the augmented-type summation term in the forward difference of Lyapunov functions. The proposed method provides a general form of inequalities, reduces estimation gap, and utilizes delay-related information effectively, leading to less conservative and low complex stability criteria for two types of time-varying delays, as demonstrated through three examples.
Article
Automation & Control Systems
Yin Sheng, Zhigang Zeng, Tingwen Huang
Summary: This article investigates the global stability of bidirectional associative memory neural networks with discrete and distributed time-varying delays. The research focuses on the global asymptotic stability and global exponential stability of the networks, using comparison strategy and inequality techniques. The results show that the stability of the networks has certain special properties.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Zhengliang Zhai, Huaicheng Yan, Shiming Chen, Hongbing Zeng, Meng Wang
Summary: This article discusses the stability problem of generalized neural networks with time-varying delay and proposes new stability criteria that do not require additional state variables and can be expressed as linear matrix inequalities.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
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, Artificial Intelligence
Xian-Ming Zhang, Qing-Long Han, Xiaohua Ge, Bao-Lin Zhang
Summary: This article investigates the extended dissipativity of discrete-time neural networks (NNs) with time-varying delay. By constructing a new delay-dependent Lyapunov functional, the effects of the time-varying delay and its variation rate on the performance of the neural network are studied.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Guoqiang Tan, Zhanshan Wang
Summary: This paper introduces a new method to study the stability and dissipativity of neural networks with time-varying delay, obtaining sufficient conditions through a novel reciprocally convex inequality, and validating the effectiveness of the method through simulations.
Article
Mathematics, Applied
Li Jin, Yong He, Lin Jiang
Summary: The paper investigates a novel integral inequality for enhancing the generality of delay-dependent stability criteria for linear systems. By constructing a newly augmented LKF and establishing stability criteria using the proposed inequality, the paper provides a flexible and relaxed approach for analyzing stability in systems with multiple delays.
APPLIED MATHEMATICS LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Zhe-Li Yuan, Chuan-Ke Zhang, Xing-Chen Shangguan, Li Jin, Da Xu, Yong He
Summary: This study investigates the stability analysis of a delayed load frequency control system in shipboard microgrids. By dividing the delay into two regions, the system is modeled as a linear system with a switched delay. A stability criterion is proposed to analyze the relationship between the system's exponential stability and the length/frequency of large delay. The case study shows that the system can still remain stable for specific conditions of large delay, illustrating the effectiveness and advantages of the proposed method.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Automation & Control Systems
Xing-Chen Shangguan, Yong He, Chuan-Ke Zhang, Li Jin, Wei Yao, Lin Jiang, Min Wu
Summary: This study introduces an event-triggered LFC scheme for power systems, which adjusts threshold parameters using CPSs-oriented regulation rules to reduce triggering frequency and meet the power system's frequency and tie-lie power requirements, while alleviating communication burden.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2022)
Article
Automation & Control Systems
Meng Liu, Yong He, Lin Jiang
Summary: This paper investigates the stability problem of neutral systems with interval time-varying delays and nonlinear disturbances. A relaxation technique based on a binary quadratic function is proposed to obtain more relaxed stability criteria.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Automation & Control Systems
Wen-Juan Lin, Yong He, Chuan-Ke Zhang, Leimin Wang, Min Wu
Summary: This article addresses the fault detection filter design problem for discrete-time memristive neural networks with time delays. The use of an event-triggered communication mechanism and a fault weighting matrix function improves the accuracy of the FD filter. Based on Lyapunov functional theory, an FD filter is designed that ensures asymptotic stability and a prescribed H-infinity performance level for the residual system.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Mathematics, Applied
Yi-Bo Huang, Yong He
Summary: This paper presents a Bessel-type inequality in semi-inner produce spaces for stability analysis of discrete-time systems with distributed delays, aiming to obtain less conservative stability conditions. The proposed inequality, combining the Bessel inequality and pairwise orthogonal polynomials, is more general than the conventional Jensen-type inequality. A stability condition is derived based on the proposed inequality and Lyapunov-Krasovskii stability theory. A numerical example demonstrates the effectiveness of the proposed method in reducing conservatism.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
Article
Mathematics, Applied
Xu-Kang Chang, Yong He, Zhen-Man Gao
Summary: This article investigates the problem of global exponential stability of neural networks with a time-varying delay. By establishing an improved augmented delay-product-type Lyapunov-Krasovskii functional and utilizing the cross-term relationships and negative-determination lemma, a stability criterion is obtained.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Automation & Control Systems
Yong He, Chuan-Ke Zhang, Hong-Bing Zeng, Min Wu
Summary: This paper investigates the characteristics of variable-augmented-based free-weighting matrices and their application to stability analysis in systems with time-varying delay. By introducing variable-augmented-based free-weighting matrices, the higher-order terms of time-varying delay are avoided in estimating the derivative of the Lyapunov-Krasovskii functionals, leading to an improved stability condition. The common characteristics and additional functions of the variable-augmented-based free-weighting matrices are analyzed, and a basic criterion is given to remove unnecessary matrices, resulting in more efficient results.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Letter
Computer Science, Information Systems
Chuan-Ke Zhang, Ke-You Xie, Yong He, Jinhua She, Min Wu
SCIENCE CHINA-INFORMATION SCIENCES
(2023)
Letter
Computer Science, Information Systems
Hongbing Zeng, Huichao Lin, Yong He, Wei Wang
SCIENCE CHINA-INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Fei Long, Chuan-Ke Zhang, Yong He, Qing-Guo Wang, Zhen-Man Gao, Min Wu
Summary: This article improves the techniques for passivity analysis of neural networks with time-varying delay to establish a new criterion with less conservatism. It constructs a Lyapunov-Krasovskii functional (LKF) with general delay-product-type terms, develops a general convexity lemma, and obtains a hierarchical passivity criterion of less conservatism for neural networks with time-varying delay.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Yang Li, Yong He, Chuan-Ke Zhang, Min Wu
Summary: This article proposes a discrete-state decomposition technique for dissipativity problem in discrete-time singular systems with time-varying delays. By establishing the state-decomposed Lyapunov function and using zero-value equations and inequalities to bound the forward difference of the Lyapunov function, less conservative dissipativity criteria with lower computational complexity are obtained.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Proceedings Paper
Automation & Control Systems
Xiaojie Peng, Yong He
Summary: This paper addresses the containment control problem for multi-agent systems with time-varying delays. An augmented Lyapunov-Krasovskii functional is constructed to obtain less conservative containment control criterion. By employing the Wirtinger-based integral inequality and the extended reciprocally convex matrix inequality, the delay-dependent term is reconsidered. Additionally, a delay-product-type functional based on the Jensen inequality is established to further reduce conservatism. The derived criteria ensure containment consensus on the communication directed graph, the upper bound of maximum delays, and feedback gains.
2022 22ND INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2022)
(2022)
Article
Automation & Control Systems
Chen-Rui Wang, Yong He, Chuan-Ke Zhang, Min Wu
Summary: This research investigates the stability of discrete-time neural networks with a time-varying delay and presents less conservatism stability criteria to avoid high-degree polynomials. The proposed methods provide more freedom for estimation results and improve the stability conditions of the time-varying delayed networks.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Ying Zhang, Yong He, Fei Long, Chuan-Ke Zhang
Summary: This paper studies the synchronization control for delayed neural networks considering communication delay by using an input delay approach. A novel augmented Lyapunov functional is proposed to fill the gap in understanding the interaction between transmission delay and communication delay. The introduced time-dependent quadratic terms and sampling integral states result in a less conservative synchronization criterion.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
