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
Automation & Control Systems
Qiaoyu Chen, Dongbing Tong, Wuneng Zhou
Summary: The finite-time stochastic boundedness (FTSB) analysis for Markovian jumping systems with time-delays is conducted using the sliding mode control (SMC) approach. An SMC law is designed to ensure the reachability of the sliding mode surface in a finite-time. Delay-dependent criteria for FTSB are obtained, and sufficient conditions are provided using linear matrix inequalities (LMIs) to guarantee FTSB over the whole finite-time interval.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Bart Kosko
Summary: Bidirectional associative memories (BAMs) pass neural signals forward and backward through the same network of synapses. These memories can adjust synaptic weights using unsupervised learning and can be extended to arbitrary hidden layers with proper bidirectional backpropagation algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Junyi Wang, Zhanshan Wang, Xiangyong Chen, Jianlong Qiu
Summary: This paper investigates the synchronization problem of inertial neural networks with time-varying delays and generally Markovian jumping. Synchronization criteria and controllers are obtained using variable transformation method, Lyapunov-Krasovskii functionals, and linear matrix inequalities. Numerical examples confirm the effectiveness of the theoretical results.
Article
Engineering, Electrical & Electronic
Mani Kant Kumar
Summary: This paper investigates the mixed H-infinity and passivity performance analysis of interfered digital filters. A novel sufficient condition is derived and can be used for different performance indices of digital filters, including H-infinity performance and passivity performance.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Zeyu Dong, Xian Zhang, Xin Wang
Summary: This paper proposes a new method for establishing the stability of discrete-time high-order Cohen-Grossberg neural networks, achieving advantages over previous research by constructing a new model and deriving stability criteria.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Kuo-Shou Chiu, Tongxing Li
Summary: This study presents global exponential stability criteria for the BAM neural networks model with constant delay effects and investigates the existence and stability of the model in the DEGPCD system using an equivalent integral equation approach. The research highlights the significant impact of generalized piecewise constant delay on the stability of the model.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2022)
Article
Mathematics, Applied
S. Senthilraj, T. Saravanakumar, R. Raja, J. Alzabut
Summary: This paper focuses on solving the problem of stochastic genetic regulatory networks subject to mixed time delays using passivity control. By utilizing Lyapunov functional method and Jensen's integral inequality, a new set of passivity-based delay-dependent sufficient conditions in the form of LMIs is proposed to ensure the stochastic stability of the networks under impulsive perturbations. Numerical simulations are conducted to demonstrate the efficiency of the proposed method.
ADVANCES IN DIFFERENCE EQUATIONS
(2021)
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
Physics, Multidisciplinary
Adriano Barra, Giovanni Catania, Aurelien Decelle, Beatriz Seoane
Summary: In this paper, the equilibrium properties of bidirectional associative memories (BAMs) are investigated. The computational capabilities of a stochastic extension of BAM are characterized using statistical physics techniques. The phase diagram of the model at the replica symmetric level is provided, and the transition curves are analyzed as control parameters are tuned. The retrieval mechanism in BAM is explained by analogy with two interacting Hopfield models, and the potential equivalence with two coupled Restricted Boltzmann Machines is discussed.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2023)
Article
Computer Science, Artificial Intelligence
Damiem Rolon-Merette, Thadde Rolon-Merette, Sylvain Chartier
Summary: A multilayered bidirectional associative memory neural network, composed of a Multi-Feature extracting bidirectional associative memory (MF) and a modified Bidirectional Associative Memory (BAM), is proposed. The MF generates feature patterns from the original inputs, which are learned by the BAM. Experimental results show that the model can consistently learn various nonlinear tasks and the degree of nonlinearity in decision boundaries can be adjusted by manipulating network parameters.
Article
Computer Science, Artificial Intelligence
Yingying Li, Junrui Li, Jie Li, Shukai Duan, Lidan Wang, Mingjian Guo
Summary: This study introduces a novel memristive bidirectional associative memory (MBAM) network circuit based on the threshold memristor bridge circuit (T-MBC), which shows excellent flexibility and reconfigurability. Experimental results demonstrate that the MBAM neural network achieves high recognition accuracy and strong noise immunity.
Article
Engineering, Electrical & Electronic
Liping Bai, Juan Zhou
Summary: This paper focuses on the delay-dependent H-infinity control problem for uncertain singular time-varying delay systems with Markovian jumping parameters. A new Lyapunov-Krasovskii functional and bounded real lemma (BRL) are proposed, and delay decomposition approach and improved Wirtinger inequality are employed to reduce conservatism.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Mathematics, Interdisciplinary Applications
Mani Kant Kumar
Summary: This paper addresses the problem of mixed H-infinity and passivity performance analysis of digital filters subject to Markovian jumping parameters, external disturbances, time delays, and bounds of nonlinearity functions. By using Lyapunov theory and matrix decomposition technique, a novel sufficient condition is established. The proposed criterion ensures stochastic stability of the underlying system and simultaneously satisfies mixed H-infinity and passivity performance index.
FLUCTUATION AND NOISE LETTERS
(2022)
Article
Engineering, Multidisciplinary
Lan Yao, Xia Huang
Summary: In this article, the secure control of Markov jumping neural networks subject to deception attacks is studied. Two memory-based adaptive event-trigger mechanisms (AETMs) are proposed to address the limitations of network bandwidth and the impact of deception attacks. These AETMs include historical triggered data in both the triggering conditions and adaptive law, enabling adaptive adjustment of data transmission rate to alleviate the impact of deception attacks and suppress system response peaks.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Sunny Singh, Umesh Kumar, Subir Das, Jinde Cao
Summary: In this article, the global exponential stability problem of delayed Cohen-Grossberg inertial neural networks is addressed by constructing a new innovative Lyapunov functional. The proposed method, together with two different control schemes and the inequality technique, analyzes the stability of the considered second-order inertial neural networks. The dynamical behavior of the networks in this study is novel and different from the traditional reduced-order method through variable substitution. The simpler inequalities in the proposed method help achieve stability criteria in a more straightforward way compared to existing results. A numerical example is provided to validate the efficiency of the proposed method.
NEURAL PROCESSING LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Ruoyu Wei, Jinde Cao, Sergey Gorbachev
Summary: This paper focuses on the fixed-time synchronization control of quaternion-valued memristive neural networks (QVMNNs). By decomposing the QVMNNs model into four real-valued systems and designing discontinuous control schemes based on the sign function, novel criteria for fixed-time synchronization are derived using nonsmooth analysis and inequality techniques.
COGNITIVE COMPUTATION
(2023)
Article
Engineering, Electrical & Electronic
Qian Zhao, Hao Yang, Dongming Zhou, Jinde Cao
Summary: Image deblurring is a low-level vision task that aims to estimate sharp images from blurred images. Traditional CNN-based deblurring methods suffer from limitations in model performance and capturing long-range dependencies. To address these issues, we propose a hybrid architecture called CTMS, which combines CNN and transformer. CTMS effectively handles large-area blur, adapts to input content, and reduces computational burden.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Mechanical
Boqiang Cao, Xiaobing Nie, Jinde Cao, Peiyong Duan
Summary: This paper investigates the practical finite-time adaptive control problem for a class of incommensurate fractional-order nonlinear systems with external disturbances. A practical finite-time stability criterion is established for a fractional-order system, and a practical finite-time adaptive control scheme is designed by using the property of fractional-order calculus. Compared with existing control schemes, the proposed scheme reduces the fluctuation range of control signals and simplifies the design process through the use of filters and a compensated signal. Numerical simulations confirm the effectiveness of the proposed control scheme.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Tianbo Xu, Chunxia Zhu, Wenhai Qi, Jinde Cao, Jun Cheng, Kaibo Shi, Guangsheng Pan
Summary: This article focuses on the issue of finite-time analysis for fuzzy semi-Markovian jump systems (S-MJSs) with multiple disturbances. The Takagi-Sugeno fuzzy method is applied to address the nonlinear problem of closed-loop systems. Unlike existing research, this article analyzes delay, multiple disturbances, generally uncertain transition rate, and uncertain parameters in a unified S-MJSs framework.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Jinliang Liu, Nan Zhang, Yan Li, Xiangpeng Xie, Engang Tian, Jinde Cao
Summary: This article focuses on the optimal tracking control problem for a class of nonlinear networked systems subject to limited network bandwidth and unmatched disturbance. By introducing an event-triggered mechanism and a reinforcement learning-based algorithm, it is demonstrated that the stability of the concerned system can be guaranteed, and the effectiveness of the algorithm is validated through theoretical analysis and simulations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Mathematics, Applied
Qiongwen Zhang, Jun Cheng, Daixi Liao, Jinde Cao, Fawaz E. Alsaadi
Summary: This paper focuses on the design of protocol-based control for nonlinear systems with fading channels. It proposes an improved dynamic event-triggered protocol that considers historical transmitted packets to efficiently reduce triggering times while maintaining desired control performance. The time-varying fading channel is modeled as a Markov process, and a hidden Markov mode detector is used to detect the mode. Sufficient conditions are derived based on Lyapunov stability theory to achieve stochastic stability of the closed-loop system. The validity of the results is verified through an application study.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Engineering, Electrical & Electronic
Yiheng Wei, Linlin Zhao, Junguo Lu, Jinde Cao
Summary: This study constructs a circular region to approximate the stable region and derives sufficient conditions using LMI.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Mathematics, Applied
Zhizhuo Zhang, Xiaobing Nie, Jinde Cao
Summary: This study investigates the mathematical model of a multilayer viscoelastic system based on the actual structure of asphalt pavement. The study includes the derivation of the model, the proof of the existence and uniqueness of its solutions, and the error analysis of the numerical solutions.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Huimin Guan, Yang Liu, Kit Ian Kou, Jinde Cao, Leszek Rutkowski
Summary: In this paper, a distributed optimization method is proposed to solve nonlinear equations with constraints. The multiple constrained nonlinear equations are transformed into an optimization problem and solved in a distributed manner. To deal with the nonconvexity issue, a multi-agent system based on an augmented Lagrangian function is introduced and proven to converge to a locally optimal solution. Moreover, a collaborative neurodynamic optimization method is adopted to obtain a globally optimal solution. The effectiveness of the proposed method is illustrated through three numerical examples.
Article
Physics, Multidisciplinary
Yuwei Yang, Zhuoxuan Li, Jun Chen, Zhiyuan Liu, Jinde Cao
Summary: This paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence (TRELM-DROP) for accurate prediction of traffic flow. The algorithm reduces the impact of randomness in traffic flow through the Tent chaos strategy and residual correction method, and avoids weight optimization using the iterative method. A DROP strategy is introduced to improve the algorithm's ability to predict traffic flow under varying conditions.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Mathematics, Applied
Xia Zhou, Chunya Huang, Jinde Cao, Wanbing Liu, Meixuan Xi
Summary: The leader-following consensus of nonlinear multi-agent systems with Markov switching topologies under denial-of-service attacks and event-triggered control is investigated. An event-triggered strategy is applied to reduce unnecessary signal transmission, save network resources, and ensure system performance. The communication topologies are modeled as Markov switching topologies, and the transfer rates are assumed to be partially unknown. The Lyapunov direct method and stochastic analysis method are employed to establish sufficient conditions for achieving leader-following consensus. An example is provided to validate the effectiveness of the proposed methods and the correctness of the results.
Article
Mathematics, Applied
Wenjie Li, Yajuan Guan, Jinde Cao, Fei Xu
Summary: This article establishes the global stability of the disease-free equilibrium in a degenerate diffusion system involving environmental transmission and spatial heterogeneity. It provides important insights into the transmission dynamics of avian influenza virus among avian, poultry, and human populations.
APPLIED MATHEMATICS LETTERS
(2024)
Article
Mathematics, Applied
Lirui Zhao, Huaiqin Wu, Jinde Cao
Summary: This paper investigates the distributed consensus problem in multi-agent systems using fractional reaction-diffusion partial differential equations. Two novel event-triggered boundary control schemes are proposed based on Lyapunov technique and linear matrix inequalities theory to achieve consensus. The effectiveness of the control performance is verified through an example.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Computer Science, Artificial Intelligence
Hanjie Liu, Jinde Cao, Wei Huang, Xinli Shi, Xingye Zhou, Zhuoxuan Li
Summary: A data-driven multidimensional framework is proposed to evaluate pavement condition by utilizing multilayer network representation learning. The method can capture the nonlinear interactions among performance attributes and provide a more in-depth understanding of pavement service condition. Experimental results demonstrate the effectiveness of this method in multi-attribute evaluation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
