Article
Automation & Control Systems
Taotao Hu, Zheng He, Xiaojun Zhang, Shouming Zhong, Xueqi Yao
Summary: This paper investigates the delay-dependent stability analysis of fractional-order systems with time-varying delay by proposing novel fractional-order integral inequalities and designing Lyapunov-Krasovskii functions to reduce conservatism, deriving delay-dependent criteria to achieve asymptotic stability of systems with time-varying delay.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Mathematics, Interdisciplinary Applications
Xin Liu, Lili Chen, Yanfeng Zhao
Summary: This paper discusses the problem of uniform stability for a class of fractional-order fuzzy impulsive complex-valued neural networks with mixed delays in infinite dimensions for the first time. The uniqueness of the solution and criteria for uniform stability are derived using fixed-point theory, theory of differential inclusion, and set-valued mappings. Compared to related results, the approach does not require the construction of a complex Lyapunov function, reducing computational complexity.
FRACTAL AND FRACTIONAL
(2022)
Article
Computer Science, Artificial Intelligence
Hui Li, Yonggui Kao, Haibo Bao, Yangquan Chen
Summary: This paper discusses the complex-valued neural network based on CV parameters and variables, focusing on the fractional-order CVNN with linear impulses and fixed time delays. Criteria for uniform stability and existence and uniqueness of equilibrium solutions were derived using the sign function, Banach fixed point theorem, and two classes of activation functions. Three experimental simulations were presented to illustrate the correctness and effectiveness of the obtained results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jianying Xiao, Jinde Cao, Jun Cheng, Shiping Wen, Ruimei Zhang, Shouming Zhong
Summary: This article focuses on the global synchronization and stability of fractional-order quaternion-valued neural networks, proposing multiple and flexible criteria based on the Lyapunov theory and new inequalities. The effectiveness of these criteria is demonstrated through numerical examples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yao Xu, Shang Gao, Wenxue Li
Summary: This article examines the exponential stability problem of fractional-order complex multi-links networks under aperiodically intermittent control, and provides the theoretical results on how control gain and fractional derivative order affect the exponential convergence rate. The practicality of the theoretical results is demonstrated through investigating the stability of fractional-order competitive neural networks with aperiodically intermittent control and establishing a stability criterion, which is further validated through a numerical example.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Yuanchu Shen, Song Zhu, Xiaoyang Liu, Shiping Wen
Summary: This article discusses the coexistence and dynamical behaviors of multiple equilibrium points in fractional-order complex-valued memristive neural networks with delays. The conditions for the existence of multiple equilibrium points are proposed, and the stability of these points is proven using the Lyapunov function. The effectiveness of the theoretical analysis is verified through computer simulations.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Mathematics, Applied
Mani Mallika Arjunan, Pratap Anbalagan, Qasem Al-Mdallal
Summary: This paper aims to establish the uniform stability criteria for fractional-order time-delayed gene regulatory networks with leakage delays (FOTDGRNL). The existence and uniqueness of the considered systems are established using the Banach fixed point theorem. The delay-dependent uniform stability and robust uniform stability of FOFGRNLT are investigated with the help of certain analysis techniques depending on equivalent norm techniques. Two numerical examples are provided to justify the applicability of the theoretical results.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2023)
Review
Computer Science, Artificial Intelligence
Jinde Cao, K. Udhayakumar, R. Rakkiyappan, Xiaodi Li, Jianquan Lu
Summary: This study provides an exhaustive review of the dynamical studies of multidimensional FONNs in continuous/discontinuous time. It covers various neural network models and their applications in different mathematical fields. Theoretical findings from multidimensional FONNs with different types of delays are thoroughly evaluated, and stability and synchronization requirements for fractional-order NNs without delays are mentioned.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Fanghai Zhang, Zhigang Zeng
Summary: This article investigates the multistability and stabilization of fractional-order competitive neural networks with unbounded time-varying delays, deriving conditions for coexistence of equilibrium points and multiple mu-stability through analytical methods. The results enrich and improve previous findings in the field, and are demonstrated to be effective through numerical examples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Mathematics
Yilin Hao, Xiulan Zhang
Summary: This article discusses the adaptive control of uncertain fractional-order time-delay systems using a fuzzy adaptive method and robust controller design. Simulation examples demonstrate the effectiveness of the proposed method in stabilizing the system state and keeping the signals bounded within the FOTDS.
JOURNAL OF MATHEMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Chengdai Huang, Juan Wang, Xiaoping Chen, Jinde Cao
Summary: This paper investigates the bifurcation issue of a FOBAMNN with four different delays, finding that the stability of the network can be preserved by selecting smaller control delays, while Hopf bifurcation occurs once the delays exceed their critical values. The derived bifurcation results are numerically verified and the theoretical analysis is validated through simulation experiments.
Article
Mathematics, Interdisciplinary Applications
Ivanka Stamova, Trayan Stamov, Gani Stamov
Summary: In this paper, the concept of Lipschitz stability is introduced to impulsive delayed reaction-diffusion neural network models of fractional order. The stability analysis and criteria proposed in the paper extend existing results and are useful in numerous inverse problems.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Mathematics, Applied
Xiang Liu, Yongguang Yu
Summary: This paper introduces the discrete fractional-order Halanay inequality with distributed delays. Then, based on the generalized discrete fractional Halanay inequality and Lyapunov functional method, several novel Mittag-Leffler stability and synchronization conditions of discrete fractional-order neural network systems with distributed delays are derived. An example is given to illustrate one of the results.
FRACTIONAL CALCULUS AND APPLIED ANALYSIS
(2022)
Article
Mathematics, Applied
K. Udhayakumar, R. Rakkiyappan, Xiaodi Li, Jinde Cao
Summary: This paper investigates the multiple psi-type stability of fractional-order quaternion-valued neural networks and provides new conditions for the existence of multiple equilibrium points. The psi-type stability of the proposed neural networks is studied using fractional calculus theory and derivative techniques, and the effectiveness of the theoretical results is demonstrated through numerical simulations.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Fanghai Zhang, Zhigang Zeng
Summary: This article discusses the multistability and attraction of fractional-order neural networks with unbounded time-varying delays. Multiple sufficient conditions are provided for the coexistence of equilibrium points with concave-convex activation functions. The criteria for Mittag-Leffler stability can be simplified to M-matrix and the extension of attraction basin is shown to be independent of the magnitude of delays. Three numerical examples are given to demonstrate the validity of the theoretical results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Hongmei Zhang, Hai Zhang, Weiwei Zhang, Jinde Cao
Summary: This article investigates the synchronization conditions of Riemann-Liouville fractional time-varying delayed neural networks with parametric uncertainty. A novel Lyapunov-Krasovskii functional with double integral terms is proposed, which reduces the conservatism of the results. The new synchronization criteria are established and characterized as linear matrix inequalities using convex combination technique, fractional calculus properties, and delay-decomposing method. Finally, two numerical simulations verify the correctness of the results.
NEURAL PROCESSING LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Shasha Xie, Wenhua Qian, Rencan Nie, Dan Xu, Jinde Cao
Summary: In non-homogeneous texture synthesis, it is important to maintain consistent overall visual characteristics when extending local patterns. Existing methods focus on local visual features and neglect the crucial relative position features for non-homogeneous texture synthesis. Therefore, modeling pixel dependence is desired to enhance synthesis performance. This paper proposes a non-homogeneous texture extended synthesis model (GAGCN) that combines generate adversarial network (GAN) and graph convolutional network (GCN) to ensure synthesis results from both local detail structure and overall structure.
IET IMAGE PROCESSING
(2023)
Article
Mathematics, Applied
Chengdai Huang, Huanan Wang, Jinde Cao
Summary: This paper presents novel results on fractional order-induced bifurcation of a tri-neuron fractional-order neural network (FONN) with delays and instantaneous self-connections. The authors systematically analyze the order as a bifurcation parameter and establish the order critical value through an implicit function array. The derived results show that once the fractional order exceeds the bifurcation critical value, the system's stability is destroyed and Hopf bifurcation occurs. Two numerical experiments are conducted to validate the developed key findings.
Article
Automation & Control Systems
Meirong Wang, Jianqiang Hu, Jinde Cao
Summary: This paper addresses the resilient consensus control problem of a linear multi-agent system subject to false data injection attacks. The proposed algorithm utilizes an extended state observer to estimate the injected false data and design a distributed resilient consensus control algorithm to mitigate the attacks. Sufficient conditions are derived for both undirected and directed multi-agent systems under false data injection attacks.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Automation & Control Systems
Yiheng Wei, Yuquan Chen, Xuan Zhao, Jinde Cao
Summary: This study proposes a framework for processing gradient algorithms based on the nabla fractional-order system theory. The gradient algorithm is transformed into a nabla fractional-order dynamic system, and it is designed using control theory and analyzed using the Lyapunov theory, which improves the performance of the algorithm. Three types of algorithms are built in this study, and a comprehensive simulation study is conducted to verify the correctness, usefulness, and practicality of the framework.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Mathematics, Applied
Zhimin Han, Yi Wang, Jinde Cao
Summary: The study found that the increase in individuals' contact heterogeneity may lead to complex dynamics of disease behavior, breaking the correlation between initial growth and the basic reproduction number. Analyzing the infected density monotonicity in networks with bimodal degree distribution, sufficient or necessary conditions were derived. In networks with arbitrary degree distribution, regularities in the initial growth behavior were discovered.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Engineering, Electrical & Electronic
Hongling Qiu, Hongxing Wang, Yongping Pan, Jinde Cao, Heng Liu
Summary: This paper investigates the stability and L-8-gain of positive fractional-order singular systems with time-varying delays. An equivalent auxiliary system is developed to avoid singularity problem, and a criterion ensuring the positivity of delayed systems is established. The asymptotic stability and L-8-gain of systems are analyzed using the positivity and monotonicity of system solutions.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Mathematics, Applied
Shuting Chen, Jinde Cao, Ivanka Stamova
Summary: This paper investigates the time fractional Keller-Segel system with a small parameter. The heteroclinic orbit of the degenerate time fractional Keller-Segel system is demonstrated using the fractional order traveling wave transformation and constructing a suitable invariant region. The persistence of traveling waves in the system with a small parameter is further illustrated. These results are mainly based on the application of geometric singular perturbation theory and Fredholm theorem, which are fundamental theoretical frameworks for dealing with problems of complexity and high dimensionality. The asymptotic behavior is depicted by the asymptotic theory to illustrate the rate of decay for traveling waves.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Xinyu Li, Zunshui Cheng, Jinde Cao, Fawaz E. Alsaadi
Summary: This paper analyzes the stability and bifurcation of neural networks with distributed delayed hyper-strong kernels. It provides conditions for stability and Hopf bifurcation by discussing the characteristic equations of delayed kernels with different strengths. The study uses normal theory and center manifold theory to determine the stability and direction of bifurcating periodic solutions, and verifies the results through numerical simulation.
NEURAL PROCESSING LETTERS
(2023)
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
Automation & Control Systems
Jing Wang, Jiacheng Wu, Hao Shen, Jinde Cao, Leszek Rutkowski
Summary: This article presents a decentralized learning control method for partially unknown nonlinear systems with asymmetric control input constraints and mismatched interconnections. The method utilizes integral reinforcement learning to avoid system drift dynamics and uses a critic neural network to obtain the approximated value function. A novel dynamic event-triggering condition is also introduced to determine the occurrence of an event. The effectiveness of the proposed method is verified through experiments on nonlinear interconnected and power systems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Mathematical & Computational Biology
Bo Kou, Jinde Cao, Wei Huang, Tao Ma
Summary: This paper combines machine learning and mechanical-empirical models to study the feature selection affecting the rutting evolution and rutting depth model of semi-rigid asphalt pavement. The results show that the R-F model has more accurate prediction ability and better generalization ability, without the need for complex data preprocessing and noise reduction, greatly improving the applicability and accuracy of the existing model framework.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)
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
Engineering, Civil
Jian Gong, Yuan Zhao, Jinde Cao, Jianhua Guo, Mahmoud Abdel-Aty, Wei Huang
Summary: This paper presents a strategy for improving traffic efficiency, driving safety, and fuel economy using emerging technologies of connected and automated vehicles at intersections. A virtual spring coordination system is established to regulate the movements of conflicting vehicles, and a distributed control protocol is designed to achieve the desired motion state. Simulation results demonstrate the effectiveness and superiority of the proposed strategy.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(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)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.
