Article
Mathematics, Applied
Cong Zou, Bing Li, Feiyang Liu, Bingrui Xu
Summary: This paper addresses the issue of event-triggered it-state estimation for a class of Markovian jumping neural networks with mixed delays. An event-triggered mechanism with mode dependence is adopted, and a criterion is obtained for ensuring the stochastic it-stability performance of the error system.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
Article
Mathematics, Interdisciplinary Applications
M. Hymavathi, G. Muhiuddin, M. Syed Ali, Jehad F. Al-Amri, Nallappan Gunasekaran, R. Vadivel
Summary: This paper investigates the global exponential stability of fractional order complex-valued neural networks with leakage delay and mixed time varying delays. Sufficient conditions for global exponential stability are established by constructing a proper Lyapunov-functional. The stability conditions are expressed in terms of linear matrix inequalities and the effectiveness of the obtained results is illustrated through two numerical examples.
FRACTAL AND FRACTIONAL
(2022)
Article
Mathematics, Applied
C. T. Tinh, P. T. Nam, T. N. Nguyen, H. Trinh
Summary: We introduce a new method to find an alpha-exponential estimate for a class of positive discrete time-delay systems. Instead of using a constant factor, our novel approach involves a decreasing factor function to provide a smaller estimate for the system. The effectiveness of our results is demonstrated through a numerical example.
APPLIED MATHEMATICS LETTERS
(2021)
Article
Automation & Control Systems
Shuhao Cao, Xian Zhang, Tianqiu Yu, Xiaona Yang
Summary: This article investigates the global h-stability for differential positive systems with multiple discrete time-varying delays and constant distributed delays. By proposing a direct analysis method, a sufficient condition for the global h-stability is obtained and represented in simple inequality form for easy handling.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Mathematics
Shouwei Zhou, Jiangliu Gu, Changchun Shen, Min Jiang
Summary: This paper focuses on the reachable set estimation for uncertain Markovian jump systems with time-varying delays and disturbances, aiming to find a proper method to minimize the no-ellipsoidal bound of the reachable set. By utilizing an augmented Lyapunov-Krasovskii functional and dividing the time-varying delay into nonuniform subintervals, more general delay-dependent stability criteria are derived. An optimized integral inequality is used to handle integral terms, and numerical examples are presented to demonstrate the effectiveness of the theoretical results.
JOURNAL OF MATHEMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Yang Cao, K. Maheswari, S. Dharani
Summary: This study addresses the problem of estimator design for stochastic discrete-time semi-Markov jump neural networks (NNs) with both quantization and mixed time delays. The asymptotic stability criteria are acquired by establishing an appropriate Lyapunov functional using summation inequalities in both single and double forms for the semi-Markov jump networks. By utilizing the Lyapunov functional technique, explicit expressions for the gain are proposed. Two examples are numerically exploited to demonstrate the usefulness of the new methodology.
NEURAL PROCESSING LETTERS
(2023)
Article
Automation & Control Systems
Junyi Wang, Zewen Ji, Hongli Xu, Jianlong Qiu, Yang Jiang
Summary: This article focuses on the finite-time sampled-data H infinity control problem of Markovian jumping linear systems with mode-dependent interval time-varying delays. Delay-dependent conditions for finite-time sampled-data control are obtained by using affine Bessel-Legendre inequality and appropriate mode-dependent Lyapunov-Krasovskii functional. Finally, the feasibility of the proposed method is demonstrated through two examples.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2022)
Article
Mathematics, Interdisciplinary Applications
N. Boonsatit, R. Sugumar, D. Ajay, G. Rajchakit, C. P. Lim, P. Hammachukiattikul, M. Usha, P. Agarwal
Summary: This article examines the mixed Script capital H-infinity and passivity synchronization of Markovian jumping neutral-type complex dynamical network (MJNTCDN) models with randomly occurring coupling delays and actuator faults. Novel Lyapunov-Krasovskii functional (LKF) is constructed to verify the stability of the error model and performance level, using Jensen's inequality and a new integral inequality. Sufficient conditions for the synchronization error system (SES) are given in terms of linear matrix inequalities (LMIs), with numerical illustrations provided to exhibit the usefulness of the obtained results.
Article
Mathematics, Applied
JunMin Park, Nam Kyu Kwon, Seok Young Lee
Summary: This paper addresses the stability analysis of linear discrete-time systems with time-varying delays. To reduce conservatism in stability criteria, the paper proposes extended affine Bessel summation inequalities that provide affine upper bounds of an extended summation quadratic function. It also provides notes on the correlation among several summation inequalities, demonstrating that an increase in the degree of the developed affine Bessel summation inequalities only reduces conservatism. Two numerical examples effectively demonstrate the reduction of conservatism due to the proposed summation inequalities in terms of stability regions expressed as delay bounds.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Automation & Control Systems
Tran Ngoc Nguyen, Cao Thanh Tinh, Phan T. Nam
Summary: This paper presents a new method for finding alpha-exponential state estimates for a class of positive systems with bounded time-varying delays. The novel idea is to construct a new class of comparison systems to derive a smaller factor vector, resulting in a tighter alpha-exponential state estimate. The effectiveness of the method is demonstrated through a numerical example.
Article
Computer Science, Artificial Intelligence
Cong Zou, Bing Li, Shishi Du, Xiaofeng Chen
Summary: This paper discusses the issue of H-infinity state estimation in Markovian jumping neural networks under the Round-Robin protocol scheduling, proposing a protocol-dependent state estimator model and establishing sufficient conditions for guaranteeing the asymptotic stability of the state estimation. The validity of the proposed model is demonstrated through numerical examples and simulations.
NEURAL PROCESSING LETTERS
(2021)
Article
Computer Science, Information Systems
Ting Cai, Pei Cheng
Summary: This paper investigates the exponential stability of nonlinear discrete-time systems with stochastic impulses and Markovian jump. The conditions for exponential stability of the pth moments are established. The effectiveness of the proposed method is further verified with three examples.
Article
Mathematics
Issaraporn Khonchaiyaphum, Nayika Samorn, Thongchai Botmart, Kanit Mukdasai
Summary: This research investigates finite-time passivity analysis of neutral-type neural networks with mixed time-varying delays. New sufficient conditions for finite-time stability and passivity are proposed and demonstrated through numerical examples. The proposed criteria are less conservative than prior studies in terms of larger time-delay bounds.
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
Automation & Control Systems
Qiwei Liu, Huaicheng Yan, Hao Zhang, Xisheng Zhan, Kaibo Shi
Summary: This article investigates the problem of exponential synchronization for memristor-based neural networks with mixed time-varying delays and parameter perturbations. A periodically intermittent control protocol is designed to guarantee the exponential synchronization between two networks. The exponential synchronization criteria for these networks under the proposed controller are obtained using nonsmooth analysis, Halanay inequality, and Lyapunov theory. The synchronization of these networks is considered within the framework of a second-order system directly, which is different from existing literature.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Lei Zou, Zidong Wang, Jun Hu, Hongli Dong
Summary: This article focuses on the state estimation problem for delayed complex networks subject to intermittent measurement outliers. A novel multiple-order-holder approach is proposed to resist the effects of the outliers, and sufficient conditions for bounded estimation error are provided.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Editorial Material
Plant Sciences
Chunhui Ni, Yurong Liu, Yonggang Liu, Huixia Li, Mingming Shi, Ming Zhang, Bian Han
Article
Automation & Control Systems
Xi Li, Qiankun Song, Yurong Liu, Fuad E. Alsaadi
Summary: This article presents the Hurwicz model of the zero-sum uncertain differential game with jump based on uncertainty theory. It formulates a dynamic system using an uncertain differential equation that satisfies both the canonical Liu process and V-jump uncertain process. An equilibrium equation for solving the saddle-point of the game is proposed. Furthermore, the article analyzes the game with a linear dynamic system and quadratic objective function. Finally, it describes a resource extraction problem using the theoretical results.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Guijun Ma, Zidong Wang, Weibo Liu, Jingzhong Fang, Yong Zhang, Han Ding, Ye Yuan
Summary: This article proposes a two-stage integrated method for predicting the remaining useful life (RUL) of lithium-ion batteries. In the first stage, a convolutional neural network (CNN) is used to estimate the cycle life of each battery, and a similar degradation mode is chosen for capacity identification. In the second stage, a personalized prediction is made using the identified parameters. Experimental results demonstrate the superiority of this method over standard CNN-based and GPR-based prediction methods.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Xin Wang, Xuanming Zhang, Jianjun Zou, Shaozhe Wang, Junjie Huang, Shifeng Li, Yongming Li, Yurong Liu, Min Hu, Yubin Gong, Edl Schamiloglu, B. N. Basu, Zhaoyun Duan
Summary: In this experiment, an S-band MW-level metamaterial-inspired klystron using all-metal complementary electric split ring resonators (CeSRRs) was successfully realized. The miniaturized structure of this klystron has a volume of only 0.44 of conventional counterparts. In the hot-test, the klystron delivered a maximum output power of 5.51 MW, with a gain of 55.6 dB and electronic efficiency of 57.4% at 2.852 GHz. This compact metamaterial-inspired klystron has potential applications in proton therapy facilities, tokamaks for the low-hybrid wave heating, and accelerators.
IEEE ELECTRON DEVICE LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Yurong Liu, Guangpan Lu, Likang Guo, Yanfang Zhang, Ming Chen
Summary: A flexible pressure sensor with co-planar electrodes was fabricated using piezoelectric nanocomposites made from PDMS and ZnO nanotetrapods (ZNTs) fillers. The sensors showed a linear response to external pressure and increased sensitivity with higher growth temperature and ZNTs filler concentration. The sensor demonstrated potential applications in hand gesture and speech recognition.
IEEE SENSORS JOURNAL
(2023)
Article
Automation & Control Systems
Qinyuan Liu, Zidong Wang, Hongli Dong, Changjun Jiang
Summary: In this article, the state estimation problem for networked systems with energy harvesting technologies is investigated. A binary encoding scheme is utilized to transmit the measurement results, which are quantized into a bit string and transmitted via memoryless binary symmetric channels. A minmax robust estimator is designed to minimize the worst-case covariance of the estimation error. The influence of the length of the bit stream on the transmission rate and estimation performance is discussed, and conditions for the boundedness of the proposed estimator are provided.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Chunyu Li, Zidong Wang, Weihao Song, Shixin Zhao, Jianan Wang, Jiayuan Shan
Summary: This article investigates the resilient unscented Kalman filtering fusion issue for a class of nonlinear systems under the dynamic event-triggered mechanism. The dynamic event-triggered scheme is capable of scheduling data transmission frequency more efficiently, reducing communication burden and energy consumption. Furthermore, the sequential covariance intersection fusion strategy is introduced to solve the problem of computing cross covariance between local filters.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Shengkun Jiang, Xianfeng Tang, Silong Huang, Zhifang Lyu, Zhanliang Wang, Tao Tang, Huarong Gong, Yubin Gong, Zhaoyun Duan
Summary: To develop a high power and compact terahertz (THz) sheet beam traveling-wave tube (TWT), an all metal metamaterial (MTM)-inspired slow wave structure (SWS) is proposed. The MTM-inspired SWS exhibits advantages such as high interaction impedance, double beam tunnels, and compactness. Through simulation, it is predicted that the maximum output power of the 0.22 THz TWT with double sheet beams can reach 400 W with a 3-dB bandwidth of 5.4 GHz, while having a total length of only 29.2 mm.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2023)
Article
Medicine, General & Internal
Shuang Liu, Limei Yuan, Jinzhu Li, Yurong Liu, Haibo Wang, Xingye Ren
Summary: The aim of this research was to explore the diagnostic value of circDENND4C in EOC and the corresponding mechanism. The expression of circDENND4C and miR-200b/c in tissues, serum, and cell lines of EOC were analyzed. It was found that circDENND4C was lowest while miR-200b/c was highest in EOC tissues and serums. Furthermore, circDENND4C was involved in the malignant progression of EOC by suppressing cell proliferation and stimulating apoptosis through downregulating miR-200b/c. Serum circDENND4C showed a higher specificity and accuracy than serum CA125 or HE4 in EOC diagnosis.
ANNALS OF MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Jun-Yi Li, Zidong Wang, Renquan Lu, Yong Xu
Summary: This article studies the cluster synchronization control problem for discrete-time complex dynamical networks under constrained bit rate. A bit-rate model is presented to quantify the limited network bandwidth and evaluate its effects on the control performance. Sufficient conditions are proposed to ensure boundedness of the error dynamics and the fundamental relationship between bit rate and performance index is established. Two optimization problems are formulated to design synchronization controllers and co-design issues are discussed to reduce conservatism. The developed synchronization control scheme is validated through simulation examples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Wei Chen, Zidong Wang, Derui Ding, Xiaojian Yi, Qing-Long Han
Summary: This article discusses the problem of distributed state estimation over wireless sensor networks, introduces a new distributed state estimator, and systematically discusses the probability distribution of energy level. Furthermore, the optimal estimator gain is derived by minimizing the trace of the estimation error covariance, and the convergence of the minimized upper bound of the expected estimation error covariance is analyzed.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Chuang Wang, Zidong Wang, Qing-Long Han, Fei Han, Hongli Dong
Summary: In this article, a novel leader-follower-based particle swarm optimization (LFPSO) algorithm is proposed, which maintains the diversity of the particle population while improving the possibility of escaping from the locally optimal solution. Experimental results demonstrate that the proposed algorithm significantly improves the accuracy and convergence rate of conventional particle swarm optimization algorithms, and its superiority is verified in denoising real-time signals in an oilfield pipeline network.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Kaiqun Zhu, Zidong Wang, Guoliang Wei, Xiaohui Liu
Summary: This article investigates the adaptive neural network-based set-membership state estimation problem for a class of nonlinear systems subject to bit rate constraints and unknown-but-bounded noises. A bit rate allocation mechanism is proposed to relieve the communication burden and improve state estimation accuracy. An NN-based set-membership estimator is designed using the NN learning method, relying upon a prediction-correction structure. The existence of adaptive tuning parameters and set-membership estimators is ensured, and the convergence of NN weights is analyzed.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xin Luo, Yurong Zhong, Zidong Wang, Maozhen Li
Summary: This study proposes an ASNL model for handling large-scale undirected networks, which can efficiently represent incomplete and imbalanced data of SHDI matrices, and has fast model convergence and high computational efficiency. Empirical studies on four SHDI matrices demonstrate that ASNL significantly outperforms other models in prediction accuracy and computational efficiency.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
