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
Mathematics, Applied
El Abed Assali
Summary: This paper investigates the global exponential stability of a class of Clifford-valued recurrent neural networks with time-varying delays and distributed delays. The existence of equilibrium point for Clifford-valued recurrent neural networks is established based on Brouwer's fixed point theorem. By using inequality technique and the method of the Clifford-valued variation parameter, novel assertions are given to ensure the global exponential stability of the model, which complement some previous works. The effectiveness of this approach is illustrated with a numerical example.
COMPUTATIONAL & APPLIED MATHEMATICS
(2023)
Article
Mathematics, Applied
Yonghui Chen, Yu Xue, Xiaona Yang, Xian Zhang
Summary: This paper focuses on the global exponential stability in Lagrange sense (GESLS) of quaternion memristive neural networks (QMNNs) with leakage delays, unbounded distributed delays, and time-varying discrete time delays. Instead of decomposing the QMNN into real-valued memristive neural networks (RMNNs) or complex-valued memristive neural networks (CMNNs), the paper considers the QMNN as a whole and provides a sufficient condition related to time delays to ensure GESLS. The proposed method has the advantages of not requiring Lyapunov-Krasovskii functional (LKF) and being applicable to different types of memristive neural networks.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Automation & Control Systems
Bing Li, Yuwei Cao, Yongkun Li
Summary: In this paper, a class of octonion-valued stochastic recurrent neural networks with time-varying delays is considered. The existence, uniqueness, and global exponential stability of almost automorphic solutions in distribution are proved using Banach fixed point theorem and inequality technique. The results obtained in this study are new. An illustrative example is also provided to demonstrate the effectiveness of the results.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Mathematics, Applied
G. Rajchakit, R. Sriraman, N. Boonsatit, P. Hammachukiattikul, C. P. Lim, P. Agarwal
Summary: This paper investigates the Clifford-valued recurrent neural network models and their global exponential stability in the Lagrange sense. By dividing the original Clifford-valued RNN model into real-valued models, sufficient conditions for achieving global exponential stability are obtained using Lyapunov stability theory and analytical techniques. Two examples are provided to illustrate the results, along with a discussion on their implications.
ADVANCES IN DIFFERENCE EQUATIONS
(2021)
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
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, Applied
Qinghua Zhou, Li Wan, Hongshan Wang, Hongbo Fu, Qunjiao Zhang
Summary: Due to the inability to convert Cohen-Grossberg neural networks with multiple time-varying delays and distributed delays into vector-matrix forms, the stability results and conditions in the linear matrix inequality forms are relatively few and missing. This paper addresses the issue by investigating the exponential stability of the networks and providing sufficient conditions in the linear matrix inequality forms. Two examples are used to demonstrate the effectiveness of the theoretical results.
Article
Mathematics
Wen Lv, Bing Li
Summary: In this paper, Clifford-valued fuzzy neural networks with proportional delays and Clifford numbers as leakage term coefficients are investigated. By utilizing the Banach fixed point theorem and differential inequality technique, the existence, uniqueness, and global attractivity of pseudo almost periodic solutions for these networks are established using a direct method. A numerical example is provided to demonstrate the feasibility of the results, which are novel.
Article
Computer Science, Artificial Intelligence
Liqun Zhou
Summary: This paper presents global exponential dissipativity criteria for impulsive recurrent neural networks with proportional delays, providing global attractive sets and global exponential attractive sets. By introducing adjustable parameters and designing multiple Lyapunov functionals, the criteria improve and extend earlier dissipativity criteria, expanding the range of attractive sets. Several numerical examples validate the results and demonstrate their independence.
NEURAL PROCESSING LETTERS
(2021)
Article
Automation & Control Systems
Xiaonan Liu, Minghui Song, Yonggui Kao
Summary: This paper investigates the synchronization between two hyperbolic coupled networks (HCNs) with time-varying delays using aperiodically intermittent pinning control (AIPC). Sufficient criteria for HCNs with internal delays only and with hybrid delays are obtained based on a Lyapunov function with a piecewise continuous function. It is found that HCNs with hybrid delays have a slower convergence speed compared to those with internal delays only. Additionally, two simulation results are presented to validate the theoretical findings.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Yannan Xia, Xiaofeng Chen, Dongyuan Lin, Bing Li, Xujun Yang
Summary: In order to address the non-commutativity multiplication issue of quaternions, this study proposes the establishment of commutative quaternion-valued neural networks (CQVNNs) with time delays on time scales. By applying the multiplication rules of commutative quaternion, CQVNNs are transformed into two complex-valued neural networks, thereby combining discrete-time and continuous-time CQVNNs in a unified framework. Furthermore, sufficient criteria for the global exponential stability of CQVNNs are investigated using matrix measure and inequalities on time scales. Finally, the feasibility and validity of the obtained results are verified through two numerical examples.
NEURAL PROCESSING LETTERS
(2023)
Article
Mathematics
Wenjun Dong, Yujiao Huang, Tingan Chen, Xinggang Fan, Haixia Long
Summary: This study examines the local stability of quaternion-valued neural networks, which is crucial for the application of associative memory and pattern recognition. The research proposes conditions to ensure the existence of multiple equilibrium points and stable equilibria in quaternion-valued neural networks, improving and extending the existing results.
Article
Computer Science, Interdisciplinary Applications
Shuang Chang, Yantao Wang, Xian Zhang, Xin Wang
Summary: In this article, the global exponential stability (GES) of inertial neural networks (INNs) is directly analyzed by proposing a new parameterized method. The parameterized representations of the states of neurons and their derivatives in the INNs are first given by introducing the relevant parameters. The sufficient conditions for the GES of the considered INNs are obtained using the inequality technique.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)
Article
Computer Science, Artificial Intelligence
Song Zhu, Jiahui Zhang, Xiaoyang Liu, Mouquan Shen, Shiping Wen, Chaoxu Mu
Summary: This article analyzes the multistability and robustness of competitive neural networks with time-varying delays. Sufficient conditions are proposed based on the geometry of activation functions to determine the existence of equilibrium points and their stability. The conclusions proposed in this article are easy to verify and enrich the existing theories.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Gang Bao, Zhigang Zeng
NEURAL PROCESSING LETTERS
(2018)
Article
Automation & Control Systems
Qiang Xiao, Zhenkun Huang, Zhigang Zeng
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2019)
Article
Computer Science, Hardware & Architecture
Shiping Wen, Shuixin Xiao, Yin Yang, Zheng Yan, Zhigang Zeng, Tingwen Huang
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Fanghai Zhang, Zhigang Zeng
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2019)
Article
Automation & Control Systems
Shiping Wen, Rui Hu, Yin Yang, Tingwen Huang, Zhigang Zeng, Yong-Duan Song
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Yanyi Cao, Yuting Cao, Shiping Wen, Tingwen Huang, Zhigang Zeng
Article
Computer Science, Artificial Intelligence
Yufeng Zhou, Zhigang Zeng
Article
Computer Science, Artificial Intelligence
Minghui Dong, Shiping Wen, Zhigang Zeng, Zheng Yan, Tingwen Huang
Article
Computer Science, Artificial Intelligence
Zhuoling Li, Minghui Dong, Shiping Wen, Xiang Hu, Pan Zhou, Zhigang Zeng
Article
Mathematics, Applied
Sen Zhang, Jiahao Zheng, Xiaoping Wang, Zhigang Zeng
Summary: This study presents a novel non-equilibrium Hindmarsh-Rose neuron model with memristive electromagnetic radiation effect. Through numerical simulations and hardware experiments, it is demonstrated that this model exhibits complex dynamics and high security, making it suitable for real-world applications.
Article
Automation & Control Systems
Leimin Wang, Zhigang Zeng, Ming-Feng Ge
Summary: This paper presents a unified framework for designing sliding-mode control to stabilize delayed memristive neural networks (DMNNs). It is proven that under this framework, the system responses can reach and stay on the designed sliding-mode surface in finite and fixed time. Additionally, the designed sliding-mode control can reject external disturbances effectively.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Mathematics, Interdisciplinary Applications
Sen Zhang, Jiahao Zheng, Xiaoping Wang, Zhigang Zeng
Summary: A novel HR neuron model with memristive electromagnetic induction is proposed in this paper, exhibiting complex dynamics and generating hidden attractors and multistability phenomenon. The detailed investigation includes bifurcation diagrams, Lyapunov exponents, time series, attraction basins, and SE complexity. Circuit simulations and hardware experiments are conducted to demonstrate the theoretical analyses, and a pseudorandom number generator is designed using chaotic sequences from the memristive HR neuron model.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Computer Science, Artificial Intelligence
Depeng Li, Tianqi Wang, Junwei Chen, Kenji Kawaguchi, Cheng Lian, Zhigang Zeng
Summary: This paper investigates a novel paradigm called multi-view class incremental learning (MVCIL), which addresses the challenges of catastrophic forgetting and interference in multi-view learning. The paper proposes a randomization-based representation learning technique and selective weight consolidation to tackle these challenges. Extensive experiments validate the effectiveness of the approach.
INFORMATION FUSION
(2024)
Article
Automation & Control Systems
Shiping Wen, Huaqiang Wei, Yin Yang, Zhenyuan Guo, Zhigang Zeng, Tingwen Huang, Yiran Chen
Summary: This paper presents a complete solution for the hardware design of a memristor-based MLSTM network, utilizing parameter sharing mechanism and efficient implementation of memristor crossbars to reduce hardware design scale. Experimental results validate the effectiveness of the system.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Junwei Sun, Gaoyong Han, Zhigang Zeng, Yanfeng Wang
IEEE TRANSACTIONS ON CYBERNETICS
(2020)
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.
